Publication List

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1. Patents

  1. US20220119536A1. Agnostic trkb binding molecules for the treatment of eye diseases. Published 2022-04-21.
  2. US12110335B2. Bispecific anti-VEGF and anti-TrkB binding molecules for the treatment of eye diseases. Granted 2024-10.
  3. US11572412B2. Anti-Sirp-alpha antibodies. Granted 02/2023.
  4. US20220411533A1. Tri-specific binding molecules. Granted 04/2025
  5. US20250163157A1. Bispecific antibodies against cd277 and a tumor-antigen. Published 05/2025

2. Books

  1. Kumar, S. and Nixon, A. E. (eds.). Biopharmaceutical Informatics: Learning to Discover Developable Biotherapeutics. Taylor and Francis/CRC Press, 2025, New York, ISBN: 9781032291673. DOI: 10.1201/9781003300311
  2. Sulea, T., Kumar, S., and Kuroda, D. (eds.) Progress and challenges in computational structure-based design and development of biologic drugs. 2024. E-book on the research topic published by Frontiers in Molecular Biosciences. Lausanne: Frontiers Media SA. DOI: 10.3389/978-2-8325-4484-6
  3. Kumar, S. and Singh, S. K. (eds.). Developability of Biotherapeutics: Computational Approaches. Taylor and Francis/CRC Press, 2015, New York. ISBN: 1482246139, 9781482246131 

3. Manuscripts under preparation, submitted or in press

  1. Mazurek, A., Davis, A., Comeau, S. R., Tsang, K., Rivera, J., Huang, Z.-F., Holt, J., Kumar, S. and Kasturirangan, S. 2023. Predicting the Purity of Multi-specific Antibodies from Sequence Using Machine Learning: Methods and Applications. Preprint available online at BioRxiv: https://www.biorxiv.org/content/10.1101/2023.12.05.570217v1.
  2. Meng, G., Mahmoudinobar, F., Nagar, H., J. Wade Davis, and Kumar, S*. 2024. An intrinsic sequence-structural profile of variable regions from marketed antibodies: Implications for mRNA delivered biotherapeutics. Manuscript under preparation. 
  3. Lee, Y., Ganesan, R., Schwämmle, V., Kumar, S. and Krawczyk, K. 2022. PATCRdb: Database of TCRs from data mining patent documents. Database. Preprint available at: PATCRdb: Database of TCRs from data mining patent documents | medRxiv

4. Published Peer Reviewed Research Articles and Book Chapters

  1. Dudzic, P., Chomicz, D., Bielska, W., Jaszczyszyn, I., Zieliński, M., Janusz, B., Wróbel, S., Pannérer, M.-M. L., Philips, A., Ponraj, P., Kumar, S., Krawczyk, K. 2025. Conserved heavy/light contacts and germline preferences revealed by a large-scale analysis of natively paired human antibody sequences and structural data. Nature Communications Biology, 8, Article number: 1110 https://doi.org/10.1038/s42003-025-08388-y.
  2. Williams, C., Mahmoudinobar, F., Davis, J. W. and Kumar, S*.2025. A Pharmacophore-Based Method for Rapid and Accurate Virtual Screening of Antibody Libraries against Antigens. Molecular Pharmaceutics.  Published Online: https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.5c00250
  3. Smaldone, A. M., Shee, Y., Kyro, G. W., Xu, C. Vu, N. P., Dutta, R., Farag, M. H., Galda, A., Kumar, S., Kyoseva, E., and Batista, V. S. 2025.  Quantum Machine Learning enables Chemical Innovation in Academia and Pharmaceutical Industries. Published as part of Chemical Reviews special issue “Quantum Computing”. https://doi.org/10.1021/acs.chemrev.4c00678 
  4. Buchanan, A., Bennett, E., Croasdale-Wood, R., Evers, A., Fennell, B., Furtmann, N., Krawczyk, K., Kumar, S., Langmead, C., Shahsavarian, M. and Tinberg, C. 2025. How to think about designing smart antibodies in the age of genAI: Integrating biology, technology, and experience. mAbs, 17, 1. Published Online: https://doi.org/10.1080/19420862.2025.2490790 
  5. Rawat, P., Prabakaran, R. Mandla, V., Sharma, D., Kumar, S., Greiff, V., and Gromiha, M. M. 2025. Investigating local sequence-structural attributes of light chain variable domains (VLs) in amyloidosis. Proteins Structure Function and Bioinformatics, 93, 9, 1451 – 1464. Published Online: https://pubmed.ncbi.nlm.nih.gov/40034034/
  6. Rajagopal, N., Choudhary, U., Tsang, K., Martin, K. P., Karadag, M., Chen, H. -T., Kwon, N.-Y., Mozdzierz, J., Horspool, A. M, Li, L., Tessier, P. M., Marlow, M. S., Nixon, A. E. and Kumar, S.* 2025. Deep learning-based design and experimental validation of a medicine-like human antibody library. Briefings in Bioinformatics, 26, 1, bbaf023. https://doi.org/10.1093/bib/bbaf023
  7. Nixon, A. E. and Kumar, S.* 2025. Biopharmaceutical Informatics: An Introduction. In: Biopharmaceutical Informatics: Learning to discover developable biotherapeutics. Kumar, S. & Nixon, A. E. (eds.), Chapter 1, pp 1-10, Taylor and Francis, Boca Raton, FL, USA. https://doi.org/10.1201/9781003300311
  8. Rawat, P., Smorodina, E., Sharma, D., Prabakaran, R., Wade, J., Akbar, R., Singh, A., Kumar, S., Greiff, V. and Gromiha, M. M. 2025. Computational biophysical analyses of antibody structure-function relationships with emphasis on therapeutic antibody-based biologics. In: Biopharmaceutical Informatics: Learning to discover developable biotherapeutics. Kumar, S. & Nixon, A. E. (eds.), Chapter 7, pp 161-200, Taylor and Francis, Boca Raton, FL, USA. https://doi.org/10.1201/9781003300311
  9. Nissley, D. A., Raybould, M. I. J., Deane, C. M. and Kumar, S.* 2025. Use of molecular simulations to understand structural dynamics of antibodies. In: Biopharmaceutical Informatics: Learning to discover developable biotherapeutics. Kumar, S. & Nixon, A. E. (eds.), Chapter 8, pp 201-227, Taylor and Francis, Boca Raton, FL, USA. https://doi.org/10.1201/9781003300311
  10. Bauer, J., Kube, S., Gupta, P., Kumar, S. 2024. Biopharmaceutical Informatics: A Strategic Vision for Discovering Developable Biotherapeutic Drug Candidates. In: Bioprocessing, Bioengineering and Process Chemistry in the Biopharmaceutical Industry. Gadamasetti, K., Kolodziej, S.A. (eds.). Chapter 14, pp 405 -436, Springer, Cham. https://doi.org/10.1007/978-3-031-62007-2_14
  11. Prabakaran, R., Rawat, P., Kumar, S. and Gromiha, M. M. 2024. Deciphering the modulatory role of mutations in protein aggregation through in silico methods. In: Protein Mutations: Consequences on structure, function, and diseases.  M. Michael Gromiha (Editor). Chapter 1, pp 3-38, World Scientific. https://doi.org/10.1142/9789811293269_0001  
  12. Chen, H.-T., Zhang, Y., Huang, J., Sawant, M., Smith, M. D., Rajagopal, N., Desai, A. A., Makowski, E., Licari, G., Marlow, M. S., Kumar, S., and Tessier, P. M. 2024. Human antibody polyreactivity is governed primarily by the heavy chain complementarity-determining regions. Cell Reports, 43, 10, 11, 114801. https://doi.org/10.1016/j.celrep.2024.114801
  13. Wossnig, L., Furtmann, N., Buchanan, A. Kumar, S. and Greiff, V. 2024. Best practices for machine learning in antibody discovery and development. Published Online. Drug Discovery Today, 29, 7, 104025.   https://www.sciencedirect.com/science/article/pii/S1359644624001508 
  14. Sulea, T., Kumar, S., Kuroda, D. 2024. Editorial for Research topic: Progress and challenges in computational structure-based design and development of biologic drugs. Frontiers in Molecular Biosciences. https://www.frontiersin.org/articles/10.3389/fmolb.2024.1360267/full
  15. Satława, T., Tarkowsk, M., Wróbel, S., Dudzic, P., Gawłowski, T., Klaus, T., Orłowsk, M., Kostyn, A., Kumar, S., Buchanan, A. and Krawczyk, K. 2024. LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies. PLOS Computational Biology 20(3): e1011881. https://doi.org/10.1371/journal.pcbi.1011881.)
  16. Bauer, J., Rajagopal, N., Gupta, P., Gupta, P., Nixon, A. E. and Kumar, S.* 2023. How Can We Discover Developable Antibody-based Biotherapeutics? Frontiers in Molecular Biosciences, 10, 1221626. (DOI: 10.3389/fmolb.2023.1221626; PMID: 37609373).
  17. Martin, K. P., Grimaldi, C., Grempler, R., Hansel, S., and Kumar, S.*.2023. Trends in industrialization of biotherapeutics: a survey of product characteristics of 89 antibody-based biotherapeutics. mAbs, 15, 1,219301. (DOI: 10.1080/19420862.2023.2191301; PMID: 36998195).
  18. Fernández-Quintero, M. L., Ljungars, A., Waibl, F., Greiff, V., Andersen, J. T., Gjølberg, T. T., Jenkins, T. P., Voldborg, B., Grav, L. M., Kumar, S., Georges, G., Hubert Kettenberger, H., Liedl, K. R., Tessier, P. M., McCafferty, J. and Laustsen, A. H. 2023. Assessing developability early in the discovery process for novel biologics. mAbs, 15, 1, 2171248 (DOI:10.1080/19420862.2023.2171248; PMID: 36823021).
  19. Licari, G., Martin, K., Crames, M., Mozdzierz, J., Marlow, M.; Karow-Zwick, A., Kumar, S.*, and Bauer, J. 2022. Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-based Biotherapeutics. Molecular Pharmaceutics (DOI: 10.1021/acs.molpharmaceut.2c00838).
  20. Raybould, M. I. J., Nissley, D. A., Kumar, S. and Deane, C. M. 2022. Computationally profiling peptide: MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies. Frontiers in Immunology, section vaccines and Molecular Therapeutics, 13, 7793. DOI: https://doi.org/10.3389/fimmu.2022.1080596 
  21. Comeau, Jr., S. R., Thorsteinson, N., and Kumar, S*. 2023. Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates. In: Kuroda, D., Tsumoto, K. (eds.)  Computer-Aided Antibody Design, Methods in Molecular Biology, 2552, 17, pp 309 – 321, ISBN978-1-0716-2608-5, Springer-Nature, New York, USA. PMID: 36346600. DOI: https://doi.org/10.1007/978-1-0716-2609-2.
  22. Thorsteinson, N., Comeau, Jr., S. R. and Kumar, S*. 2023. Structure-based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers. In: Kuroda, D., Tsumoto, K. (eds.) Computer-Aided Antibody Design, Methods in Molecular Biology, 2552, 11, pp 219 – 235, ISBN978-1-0716-2608-5, Springer-Nature, New York, USA. PMID: 36346594. DOI: https://doi.org/10.1007/978-1-0716-2609-2.
  23. Wilman, W., Wrobel, S., Bielska, W., Deszynski, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Mlokosiewicz, J., Rouyan, A., Satlawa, T., Kumar, S., Greiff, V., Krawczyk, K. 2022. Machine designed biotherapeutics: Opportunities, feasibility, and advantages of deep learning in computational antibody discovery. Briefings in Bioinformatics, bbac267. DOI: https://doi.org/10.1093/bib/bbac267.
  24. Liu, Y., Tsang, K., Mays, M., Hansen, G., Chiecko, J., Crames, M., Wei, Y., Zhou, W., Hu, J., Liu, D., Gebhard, D., Huang, Z.-F., Datar, A., Kronkaitis, A., Gueneva-Boucheva, K., Seeliger, D.,  Han, F., Sen, S., Kasturirangan, S., Scheer, J., Panavas, T., Marlow, M., and Kumar, S*. 2022. An Adapted Consensus Protein Design Strategy for Identifying Globally Optimal Biotherapeutics. mAbs, 14, 1, 2073632. (PMID: 35613320; DOI: https://doi.org/10.1080/19420862.2022.2073632).
  25. Gupta, P., Makowski, E., Kumar, S., Zhang, Y., Scheer, J. M., Tessier, P. M. 2022. Antibodies with weakly basic isoelectric points minimize trade-offs between formulation and physiological colloidal properties. Molecular Pharmaceutics. (DOI: https://doi.org/10.1021/acs.molpharmaceut.1c00373).
  26. Prabakaran, R., Rawat, P., Yasuo, N., Sekijima, M., Kumar, S.* and Gromiha, M.* 2022. Effect of Charged Mutation on Aggregation of a Pentapeptide: Insights from Molecular Dynamics Simulations. Proteins Structure Function and Bioinformatics. (DOI: 10.1002/prot.26230).
  27. Tomar, D. S., Licari, G., Bauer, J., Singh, S. K., Li, L. and Kumar, S.*. 2022. Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development. Journal of Pharmaceutical Sciences. (DOI: https://doi.org/10.1016/j.xphs.2021.10.039)
  28. Ahmed, L., Gupta, P., Martin, K. P., Scheer, J. M., Nixon, A. E. and Kumar, S.*. 2021. Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proceedings of the National Academy of Sciences of the United States of America (PNAS USA) 118, 37, e2020577118. (DOI: https://doi.org/10.1073/pnas.2020577118).
  29. Rawat, P., Ram, P., Kumar, S. and Gromiha, M. M. 2021. Exploring the sequence features determining amyloidosis in human antibody light chains. Scientific Reports 11, 1, 1-11. (DOI: 10.1038/s41598-021-93019-9).
  30. Ram, P., Rawat, P., Kumar, S. and Gromiha, M. M. 2021. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Briefings in Bioinformatics. (https://doi.org/10.1093/bib/bbab240).
  31.  Rawat, P., Ram, P., Kumar, S. and Gromiha, M. M. 2021. AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins. Biochimica et Biophysica Acta (BBA) – Proteins and Proteomics 1869 (9), 140682. (DOI: 10.1016/j.bbapap.2021.140682).  
  32. Ram, P. Rawat, P., Thangakani, A. M., Kumar, S.* and Gromiha, M.* 2021. Protein Aggregation: In silico Algorithms and Applications. Biophysical Reviews. (DOI: 10.1007/s12551-021-00778-w).
  33. Ram, P., Rawat, P., Kumar, S. and Gromiha. M. M. 2021. ANuPP: A versatile tool to predict aggregation nucleating regions in peptides and proteins. Journal of Molecular Biology, 433 (11), 166707. (https://doi.org/10.1016/j.jmb.2020.11.006). 
  34. Rawat, P., Ram, P., Ramasamy, S., Thangakani, M. A., Kumar, S. And Gromiha, M. M. 2020. CPAD 2.0:  A repository of curated experimental data on aggregating proteins and peptides. Amyloid. (https://www.tandfonline.com/doi/full/10.1080/13506129.2020.1715363) 
  35. Rawat, P., Prabakaran, R., Kumar, S., and Gromiha, M. M. 2019. AggreRATE-Pred: A mathematical model for the prediction of change in aggregation rate upon point mutation. Bioinformatics. (https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz764/5585003?guestAccessKey=d8eb348f-47b6-49d5-9afa-10a238912421)
  36. Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., and Krawczyk, K. 2019. Computational approaches to therapeutic antibody design: established methods and emerging trends. Briefings in Bioinformatics. (https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbz095/5581643?searchresult=1)
  37.  Jaffe, J., Wucherer, K., Sperry, J. B., Zou, Q., Chang, Q., Massa, M. A., Bhattacharya, K., Kumar, S., Caparon, M., Stead, D., Wright, P. A., Dirksen, A. and Francis, M. B. 2018. Effects of conformational changes in Peptide-CRM197 vaccines. Bioconjugate Chemistry (PMID: 30475601; DOI:10.1021/acs.bioconjchem.8b00661)
  38. Kumar, S.*, Roffi, K., Tomar, D. S., Cirrelli, D., Luksha, N., Meyer, D., Mitchell, J. Allen, M. J. and Li, L. 2018. Rational optimization of a monoclonal antibody for simultaneous improvements in its solution properties and biological activity. Protein Engineering Design and Selection. (PMID: 30189027; DOI: 10.1093/protein/gzy020)
  39. Tomar, D. S., Broulidakis, M., Luksha, N., Li, L. and Kumar, S*. 2017. In silico Prediction of Diffusion Interaction Parameter (kD), a Key Indicator of Antibody Solution Behaviors. Pharmaceutical Research, 35(10):193. (PMID: 30128780; DOI: 10.1007/s11095-018-2466-6)
  40. Rawat, P., Kumar, S. and Gromiha, M. M. 2018. An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins. International Journal of Biological Macromolecules, 118(Pt A):1157-1167. (PMID: 29949748; DOI: 10.1016/j.ijbiomac.2018.06.102) (https://www.sciencedirect.com/science/article/pii/S0141813018304835). 
  41. Kumar, S.*, Plotnikov, N. V., Rouse, J. C. and Singh, S. K. 2018. Biopharmaceutical Informatics: Supporting Biologic Drug Development via Molecular Modeling and Informatics. Journal of Pharmacy and Pharmacology, 70, 5, 595-608. (PMID: 28155992; DOI: 10.1111/jphp.12700) (http://onlinelibrary.wiley.com/doi/10.1111/jphp.12700/full).
  42. Tiller, K. E., Li, L., Kumar, S., Julian, M., Garde, S. and Tessier, P. M. 2017. Arginine mutations in antibody complementarity-determining regions display context-dependent affinity/specificity trade-offs.  J. Biol. Chem., 292, 40, 16638-16652 (http://www.jbc.org/content/early/2017/08/04/jbc.M117.783837).
  43. Prabakaran R., Nikam R., Kumar S., Gromiha M.M. (2017) Influence of Amino Acid Properties for Characterizing Amyloid Peptides in Human Proteome. In: Huang DS., Jo KH., Figueroa-García J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science, vol 10362. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-63312-1_47. 
  44. Prabakaran, R., Goel, D., Kumar S., and Gromiha, M. M. 2017. Aggregation prone regions in human proteome: Insights from large-scale data analyses. Proteins: Structure, Function and Bioinformatics, 85, 6, 1099-1118 (PMID:28257595; DOI: 10.1002/prot.25276)
  45. Tomar, D. S., Li, L., Broulidakis, M. P., Luksha, N. G., Burns, C. T., Singh, S. K. and Kumar, S*. 2017. In-silico Prediction of Concentration Dependent Viscosity Curves of Monoclonal Antibody Solutions. mAbs, 9, 3, 476-489. (PMID: 28125318; DOI: 10.1080/19420862.2017.1285479) (http://www.tandfonline.com/doi/full/10.1080/19420862.2017.1285479).
  46. Plotnikov, N. V., Singh, S. K., Rouse, J. C. and Kumar, S*. 2017. Quantifying Risks of Asparagine Deamidation and Aspartate Isomerization in Biopharmaceuticals by Computing Reaction Free Energy Surfaces. J. Phys. Chem. B., 121, 4, 719-730. (PMID: 28051868; DOI: 10.1021/acs.jpcb.6b11614) (pubs.acs.org/doi/abs/10.1021/acs.jpcb.6b11614).
  47. Luo, Y., Friese, O., Runnels, H. A., Zlotnick, G., Aulabaugh, A., Gore, T., Vidunas, E., Raso, S., Novikova, E., Schlittler, M., Stano, D., Dufield, R.  Shang, T., Kumar, S., Rouse, J. and Jansen, K. 2016. The dual role of lipids of the lipoproteins in Trumenba, a self-adjuvanting vaccine against 1 meningococcal meningitis B disease. AAPS J., Published Online (PMID: 27604766; DOI: 10.1208/s12248-016-9979-x) (http://link.springer.com/article/10.1208/s12248-016-9979-x).
  48. Yang, T.-C., Langford, A. J., Kumar, S., Ruesch, J. C. and Wang, W. 2016. Trimerization Dictates Solution Opalescence of a Monoclonal Antibody. J. Pharm. Sci., 105, 8, 2328-2337 (PMID: 27373839; doi: 10.1016/j.xphs.2016.05.027), published online (http://www.jpharmsci.org/article/S0022-3549(16)41463-2/abstract).
  49. Thangakani, A. M., Nagarajan, R., Kumar, S., Sakthivel, R., Velmurugan, D., and Gromiha, M. M. 2016. CPAD, Curated Protein Aggregation Database: A repository of manually curated experimental data on protein and peptide aggregation. PLoS ONE, 11, 4, e0152949, (PMID: 27043825; doi: 10.1371/journal.pone.0152949), published online (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152949).
  50. Kumar, S.*, Thangakani, A. M., Nagarajan, R., Singh, S. K., Velmurugan, D. and Gromiha, M. M. 2016. Autoimmune Responses to Soluble Aggregates of Amyloidogenic Proteins Involved in Neurodegenerative Diseases: Overlapping Aggregation Prone and Autoimmunogenic regions. Scientific Reports, 6, 22258, (PMID: 26924748; doi: 10.1038/srep22258), published online  (http://www.nature.com/articles/srep22258).
  51. Gromiha, M. M., Thangakani, A. M., Kumar, S., Velmurugan, D. 2016. Investigations of Protein Aggregation Using Sequence and Structure Based Features. Int. J. Biol. Biomol. Agri. Food Biotech. Eng., 9, 2, 214-218.
  52. Guo, J., Kumar, S., Chipley, M., Marcq, O., Gupta, D., Jin, Z., Tomar, D. S., Swabowski, C., Smith, J., Starkey, J. A., Singh, S. K. 2016. Characterization and Higher-Order Structure Assessment of a Cysteine-Based ADC: Impact of Drug Loading and Distribution. Bioconjugate Chemistry published online (PMID: 26829368, DOI: 10.1021/acs.bioconjchem.5b00603).
  53. Tomar, D. S., Kumar, S.*, Singh, S. K., Goswami, S. and Li, L. 2016. Molecular Basis of High Viscosity in Concentrated Antibody Solutions: Strategies for High Concentration Drug Product Development. mAbs, 8, 2, 216-228 (PMID: 26736022, DOI: 10.1080/19420862.2015.1128606). 
  54. Agrawal, N. J., Helk, B., Kumar, S., Mody, N., Hasige, S. A., Samra, H., Buck, P., Li, L. and Trout, B. L. 2016. Computational tool for the early on screening of monoclonal antibodies for their viscosities. mAbs, 8, 1, 43-48 (PMID: 26399600, DOI: 10.1080/19420862.2015.1099773).
  55. Kumar, S.*, Robins, R. H., Buck, P. M., Hickling, T. P., Thangakani, A. M., Li, L., Singh, S. K. and Gromiha, M. M. 2015. Biopharmaceutical Informatics: Applications of Computation in Biologic Drug Discovery and Development. In Developability of Biotherapeutics: Computational Approaches. Kumar, S. and Singh, S. K. (eds.). Taylor and Francis/CRC Press, New York, pp 3-34.
  56. Fathallah, A. M., Chiang, M., Mishra, A., Kumar, S., Xue, L., Middaugh, R. and Balu-Iyer, S.V. 2015. The effect of small oligomeric protein aggregates on the immunogenicity of intravenous and subcutaneous administered antibodies. J. Pharm. Sci., 104, 11, 3691-3702 (DOI: 10.1002/jps.24592). 
  57. Nichols, P., Li, L., Kumar, S.*, Buck, P. M., Singh, S. K., Goswami, S., Balthazor, B., Conley, T. R., Sek, D., and Allen, M. J. 2015. Rational design of viscosity reducing mutants of a monoclonal antibody: Hydrophobic versus electrostatic inter-molecular interactions. mAbs, 7, 1, 212-230 (PMID: 25559441; DOI: 10.4161/19420862.2014.985504). 
  58. Buck, P. M., Chaudhari, A., Kumar, S.* and Singh, S. K. 2014. Highly viscous antibody solutions are a consequence of network formation caused by domain-domain electrostatic complementarities: Insights from coarse-grained simulations. Molecular Pharmaceutics, 12, 1, 127-139 (DOI: 10.1021/mp500485w).
  59. Li L., Kumar, S.*, Buck, P. M., Burns, C., Lavoie, J., Singh, S. K., Warne, N. W., Nichols, P., Luksha, N., and Boardman, D. 2014. Concentration Dependent Viscosity of Monoclonal Antibody Solutions: Explaining Experimental Behavior in Terms of Molecular Properties. Pharmaceutical Research, 31, 11, 3161-3178 (DOI: 10.1007/s11095-014-1409-0; PMID: 24906598).
  60. Thangakani, A. M., Kumar, S., Nagarajan, R., Velmurugan, D. and Gromiha, M. M. 2014. GAP: Towards almost hundred percent prediction for β-strand mediated aggregating peptides with distinct morphologies. Bioinformatics, 30, 14, 1983-1990 (DOI: 10.1093/bioinformatics/bfu167; PMID: 24681906).
  61. Guo, J., Kumar, S., Prashad, A., Starkey, J. and Singh, S. K. 2014. Assessment of physical stability of an antibody drug conjugate: Impact of thiol-maleimide chemistry. Pharmaceutical Research, 31, 1710-1723 (DOI: 10.1007/s11095-013-1274-2).  
  62. Kumar, S., Zhou, S. and Singh, S. K. 2014. Metal Ion Leachates and the Physicochemical Stability of Biotherapeutic Drug Products. Current Pharmaceutical Design, 20, 8, 1173-1181 (PMID: 23713770).
  63. Buck, P. M., Kumar, S.* and Singh, S. K. 2013. Consequences of glycan truncation on Fc structural integrity. mAbs, 5, 6, 902-914 (DOI: 10.4161/mAbs.26453).
  64. Buck, P. M., Kumar, S.*, and Singh, S. K. 2013. On the role of aggregation prone regions in protein evolution, stability and enzymatic catalysis: Insights from diverse analyses. PLoS Computational Biology, 9, 10, e1003291 (DOI: 10.1371/journal.pcbi.1003291), published online. (http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003291)
  65. Gromiha, M. M., Biswal, S., Thangakani, A. M., Kumar, S., Masilamoni, G. J. And Velmurugan, D.  2013. Role of protein aggregation and interactions between α-synuclein and calbindin in Parkinson’s disease. Lecture Notes in Artificial Intelligence, 7996, 677-684 (published as part of proceedings of ICIC2013 conference held in China, July 2013).   
  66. Thangakani, A. M., Kumar, S., Velmurugan, D. and Gromiha, M. M.* 2013. Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences. BMC Bioinformatics, 14 (Supplement 8): S6 published online (http://www.biomedcentral.com/1471-2105/14/S8/S6).
  67. Jones, L. M., Zhang, H., Cui, W., Kumar, S., Sperry, J. B., Carroll, J. A. and Gross, M. L. 2013. Complementary MS methods assist conformational characterization of antibodies with altered S-S bonding networks. J. Am. Soc. Mass Spectrom., 24, 6, 835 – 845 (DOI: 10.1007/s13361-013-0582-4, published with picture on journal cover).
  68. Buck, P. M., Kumar, S.* and Singh, S. K. 2013. Insights into the potential aggregation liabilities of the b12 Fab via elevated temperature molecular dynamic simulations. Protein Engineering Design and Selection, 26, 3, 195 – 206 (DOI: 10.1093/protein/gzs099).
  69. Wang, X., Kumar, S.*, Buck, P. M. and Singh, S. K. 2013. Impact of de-glycosylation and thermal stress on conformational stability of a full length murine IgG2a monoclonal antibody: Observations from molecular dynamics simulations. Proteins Structure—function and Bioinformatics, 81, 3, 443-460 (DOI: 10.1002/prot.24202, published with picture on journal cover). 
  70. Barbosa, M. D. F. S., Kumar, S., Loughrey, H. and Singh, S. K. 2012. Biosimilars and biobetters as tools for understanding and mitigating immunogenicity of biotherapeutics. Drug Discovery Today, 17, 23-24, 1282-1288 (DOI: 10.1016/j.drudis.2012.07.003).
  71. Buck, P. M., Kumar, S.*, Wang, X., Agrawal, N. J., Trout, B. L. and Singh, S. K. 2012. Computational methods to predict of aggregation in therapeutic proteins. In Therapeutic Proteins: Methods and Protocols, Methods in Molecular Biology, Voynov, V. and Caravella, J. (eds.), Springer, USA, Vol. 899, Chapter 26, pp 425 – 451 (DOI 10.1007/978-1-61779-921-1_26).
  72. Gromiha, M. M.*, Thangakani, A. M., Kumar, S. and Velmurugan, D. 2012. Sequence analysis and discrimination of amyloid and non-amyloid peptides. Communications in Computer and Information Science, 304, 447 – 452. 
  73. Kumar, S.*, Mitchell, M. A., Rup, B., and Singh. S. K. 2012. Relationship between potential aggregation prone regions and HLA-DR binding T-cell immune epitopes: Implications for rational design of novel and follow-on therapeutic antibodies. Journal of Pharmaceutical Sciences, 101, 8, 2686-2701 (DOI 10.1002/jps.23169). This paper was featured in ‘Editors’ Picks’ by J. Pharm Sci. editors during last quarter of 2012. This paper was selected for 2014 Ebert prize from APhA-APRS.
  74. Thangakani, A. M., Kumar, S., Velmurugan, D. and Gromiha, M. M. 2012. How do thermophilic proteins resist aggregation? Proteins, 80, 4, 1003-1015. (DOI: 10.1002/prot.24002). 
  75. Zhang, A., Singh, S. K., Shirts, M. R., Kumar, S. and Fernandez, E. J. 2012. Distinct aggregation mechanisms of monoclonal antibody under thermal and freeze-thaw stresses revealed by hydrogen exchange. Pharmaceutical Research, 29, 1, 236 – 250. (DOI: 10.1007/s11095-011-0538-y)
  76. Agrawal, N. J., Kumar, S.*, Wang, X., Helk, B., Singh, S. K. and Trout, B. L. 2011.  Aggregation in Protein-Based Biotherapeutics: Computational studies and tools to identify aggregation prone regions. J. Pharm. Sci., 100, 12, 5081-5095. (DOI: 10.1002/jps.22705) (The first two authors contributed equally. One of the ten most downloaded J Pharm Sci paper during Oct-Dec.2011)
  77. Wang, X., Kumar, S.*, and Singh, S. K. 2011. Disulfide scrambling in IgG2 monoclonal antibodies: Insights from molecular dynamics simulations. Pharmaceutical Research, 28, 12, 3128-3144. (DOI: 10.1007/s11095-011-0503-9).
  78. Kumar, S.*, Singh, S. K., Wang, X., Rup, B. and Gill, D. 2011. Coupling of Aggregation and Immunogenicity in Biotherapeutics: T- and B- cell Immune Epitopes may contain Aggregation Prone Regions. Pharmaceutical Research, 28, 5, 949 – 961.  (DOI: 10.1007/s11095-011-0414-9). (Published with picture on journal cover)
  79. Kumar, S.*, Wang, X. and Singh, S. K. 2010. Identification and impact of aggregation prone regions in proteins and biotherapeutics.  In Aggregation of Therapeutic Proteins, Wang, W. and Roberts C. R. (Eds)., John Wiley and Sons, Hoboken, pp 103 – 118.
  80. Wang, X., Singh, S. K. and Kumar, S.* 2010. Potential aggregation prone regions in complementarity determining regions of antibodies and their contribution towards antigen recognition. Pharmaceutical Research, 27, 1512-1529.  
  81. Kumar, S.*, Singh, S. K. and Gromiha, M. M. 2010. Temperature dependent molecular adaptations in microbial proteins: Lessons for structure-based biotherapeutics design and development. In: Wiley Encyclopedia of Industrial Biotechnology (Ed: Flickinger, M.), Wiley, USA, vol. 7, 4647 – 4661. (DOI: 10.1002/9780470054581.eib516)
  82. Wang, X., Das, T. K., Singh, S.K. and Kumar, S*. 2009. Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies. mAbs, 1, 3, 254 – 267. (Paper highlighted on journal cover and one of the most downloaded papers within a month of publication online)
  83. Motono, C., Gromiha, M. M.* and Kumar, S*. 2008. Thermodynamic and Kinetic Determinants of Thermotoga maritima Cold Shock Protein Stability: A Structural and Dynamic Analysis. Proteins, 71, 2, 655 – 669.
  84. Kumar, S., Arya, S., and Nussinov R. 2007. Temperature dependent molecular adaptation features in proteins. In: “Physiology and Biochemistry of Extremophiles”, Charles Gerday and Nicolas Glansdorff (Eds.), American Society of Microbiology Press, Washington, DC, USA, pp 75 – 85.  
  85. Kumar, S. and Nussinov, R. 2004. Experiment guided thermodynamic simulations on reversible two state proteins: implications for protein thermostability. Biophysical Chemistry, 111, 3, 235 – 246. 
  86. Barziliai, A., Kumar, S. and Nussinov, R. 2004. Potential interrelationship between protein folding and function. Proteins, 56, 4, 635 – 649. 
  87. Wu, C. H., Nikolskaya, A., Huang, H., Yeh, L.-S. L., Natale, D. A., Vinayaka, C. R., Hu, Z. Z., Mazumder, R., Kumar, S., Kourtesis, P., Ledley, R. S., Suzek, B. E., Arminski, L., Chen, Y. Zhang, J., Cardenas, J.  L., Chung, S., Castro-Alvear, J., Dinkov, G. and Barker, W. C. 2004. PIRSF family classification system at the protein information resource. Nucleic Acids Research, 32, Database issue, D112 – D114. 
  88. Kumar, S. and Nussinov, R. 2004. Different roles of electrostatics in heat and in cold Adaptation by citrate synthase. ChemBiochem, 5, 3, 280 – 290.
  89. Kumar, S., Tsai, C. J. and Nussinov, R. 2003. Temperature range of thermodynamic stability for the native state of reversible two-state proteins. Biochemistry 42, 17, 4867 – 4873. 
  90. Gunasekaran, K, Tsai, C. J., Kumar, S., Zanuy, D. and Nussinov, R. 2003. Extended disordered proteins: targeting function with less scaffold. Trends Biochem. Sci. 28, 2, 81 – 85. 
  91. Kumar, S. and Nussinov, R. 2002. Relationship between ion pair geometries and electrostatic strengths in proteins. Biophys. J., 83, 3, 1595 – 1612. 
  92. Kumar, S., Tsai, C. J. and Nussinov, R. 2002. Maximal stabilities of reversible two state proteins.  Biochemistry, 41, 17, 5359 – 5374.  
  93. Kumar, S., Barzilai, A., Haspel, N., Sham, Y. Y., Tsai, C. J., Wolfson, H. J. and Nussinov, R. 2002. Critical building blocks in proteins: A common theme for folding and function. Recent research developments in protein folding, stability and design. M. M. Gromiha and S. Selvaraj (Editors). Research signpost, Trivandrum, India, pp 207-217. 
  94. Kumar, S. and Nussinov, R. 2002. Close-range electrostatic interactions in proteins. ChemBiochem, 3, 7, 604 – 617. 
  95. Sinha, N., Kumar, S. and Nussinov, R. 2001. Inter-domain interactions in hinge-bending transitions. Structure, 9, 1165-1181. 
  96. Kumar, S., Tsai, C. J. and Nussinov, R. 2001. Thermodynamic differences among homologous thermophilic and mesophilic proteins. Biochemistry, 40, 47, 14152-14165.  
  97. Tsai, C. J., Ma, B., Kumar, S., Wolfson, H. J. and Nussinov, R. 2001. Protein folding: Binding of conformationally fluctuating building blocks via population selection. Crit. Rev. Biochem. Mol. Biol., 36, 399-433. 
  98. Kumar, S. and Nussinov, R. 2001. How do thermophilic proteins deal with heat? Cell. Mol. Life Sci., 58, 9, 1216-1233. 
  99. Tsai, C. J., Ma, B., Sham, Y. Y., Kumar, S. and Nussinov, R. 2001. Structured disorder and conformational selection. Proteins, 44, 4, 418-427. 
  100. Kumar, S., Sham, Y. Y., Tsai, C. J. and Nussinov, R. 2001. Protein folding and function: The N-terminal fragment in adenylate kinase. Biophys. J., 80, 5, 2439-2454. 
  101. Kumar, S. and Nussinov, R. 2001. Fluctuations in ion pairs and their stabilities in proteins. Proteins, 43, 4, 433-454. 
  102. Kumar, S., Wolfson, H. J. and Nussinov, R. 2001. Protein flexibility and electrostatic interactions. IBM J. Res. Dev., 45, 3 / 4, 499-512.  
  103. Ma, B., Kumar, S., Tsai, C. J., Wolfson, H. J., Sinha, N., and Nussinov, R. 2001. Protein-Ligand interactions: Induced fit. In Robertson, S. (Ed.): Encycl. Life Sci., London, Macmillan Reference Limited, Nature publishing group / www.els.net.  
  104. Tsai, C. J., Ma, B., Sham, Y. Y., Kumar, S., Wolfson, H. J. and Nussinov, R. 2001. A hierarchical, building blocks based computational method for protein structure prediction. IBM J. Res. Dev., 45, 3 / 4, 513-523.  
  105. Kumar, S. and Nussinov, R. 2000. Fluctuations between stabilizing and destabilizing electrostatic contributions of ion pairs in conformers of the c-Myc-Max leucine zipper. Proteins, 41, 4, 485-497. 
  106. Bansal, M., Kumar, S. and Velavan, R. 2000. HELANAL – A program to characterize helix geometry in proteins. J.  Biomol. Struct. Dyn., 17, 5, 811-819.
  107. Kumar, S., Ma, B., Tsai, C. J., Sinha, N. and Nussinov, R. 2000. Folding and binding cascades: Dynamic landscapes and Population shifts. Protein Sci., 9, 1, 10-19. (Published with picture on journal cover). 
  108. Kumar, S., Tsai, C. J. and Nussinov, R. 2000. Factors enhancing protein thermostability. Protein Eng., 13, 3, 179-191. 
  109. Kumar, S., Tsai, C. J., Ma, B. and Nussinov, R. 2000. Electrostatic strengths of salt bridges in thermophilic and mesophilic glutamate dehydrogenase monomers. Proteins, 38, 4, 368-383. 
  110. Ma, B., Kumar, S., Tsai, C. J., Hu, Z. J. and Nussinov, R. 2000. Transition state ensemble in enzyme catalysis: possibility, reality or necessity? J. Theoret. Biol., 203, 4, 383-397.  
  111. Kumar, S., Tsai, C. J., Ma, B. and Nussinov, R. 2000. Contribution of salt bridges toward protein thermostability. In Sarma, R. H. and Sarma, M. H. (Eds.): J. Biomol. Struct. Dyn., Conversation 11, Issue 1, Proceedings of the 11th conversation, University of Albany, SUNY, Adenine Press, pp 79-85.
  112. Kumar, S. and Nussinov, R. 1999. Salt bridge stability in monomeric proteins. J. Mol. Biol., 293, 5, 1241-1255. 
  113. Ma, B., Kumar, S., Tsai, C. J. and Nussinov, R. 1999. Folding funnels and binding mechanisms. Protein Eng., 12, 9, 713-720. 
  114. Kumar, S., Ma, B., Tsai, C. J., Wolfson, H. J. and Nussinov, R. 1999. Folding funnels and conformational transitions via hinge bending motions. Cell Biochem.  Biophys., 31, 2, 141-164.  Review.
  115. Tsai, C. J., Kumar, S., Ma, B. and Nussinov, R. 1999. Folding funnels, binding funnels and protein function. Protein Sci., 8, 6, 1179-1188.
  116. Kumar, S. and Bansal, M. 1998. Geometrical and sequence characteristics of alpha-helices in globular proteins. Biophys. J., 75, 4, 1935-1944. 
  117. Kumar, S. and Bansal, M. 1998. Dissecting alpha-helices: Position specific sequence determinants of alpha-helices in globular proteins. Proteins, 31, 4, 460-476. 
  118. Kumar, S. and Bansal, M. 1996. Structural and sequence characteristics of long alpha-helices in globular proteins. Biophys. J., 71, 3, 1574-1586.

* Means Sandeep Kumar is the corresponding author

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