37. Systematic genome-wide discovery of host factors governing bacteriophage infectivity

Chitboonthavisuk C, Martin C, Huss P, Peters JM, Anantharaman K, Raman S

submitted

​

36. Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Nishikawa KK, Chen J, Acheson JF, Harbaugh SV, Huss P, Frenkel M, Novy N, Sieran HR, Lodewyk EC, Lee DH, Chavez JL, Fox BG, Raman S

submitted

​

35. Discovering genetic mechanisms and controlling cell states at scale

Frenkel M, Raman S

Trends in Genetics (accepted)

​

34. Bacteriophage-host interactions in microgravity onboard the International Space Station

Huss P, Chitboonthavisuk C, Meger A, Nishikawa K, Oates RP, Mills H, Holzhaus O, Raman S

submitted

​

33. Discovering chromatin dysregulation induced by protein-coding perturbations at scale

Frenkel M, Hujoel MLA, Morris Z, Raman S

submitted

​

32. A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery

Liu Z, Gillis T, Raman S, Cui Q

eLife, https://doi.org/10.7554/eLife.92262.2

​

31. Computation-guided redesign of promoter specificity of a bacterial RNA polymerase

Liu X, Meger AT, Gillis TG, Raman S

submitted

​

30. Deep metagenomic mining reveals bacteriophage sequence motifs driving host specificity

Huss P, Kieft K, Meger A, Nishikawa K, Anantharaman K, Raman S

submitted

​

29. Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors

Meger AT*, Spence MA*, Sandhu M, Jackson CJ, Raman S

Cell Systems, 15, 374-387, 2024

​

28. High-throughput approaches to understand and engineer bacteriophages

Huss P*, Chen J*, Raman S

Trends in Biochemical Sciences, https://doi.org/10.1016/j.tibs.2022.08.012, 2022

​

27. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

Leander M*, Liu Z*, Cui Q, Raman S

eLife  https://doi.org/10.7554/eLife.79932, 2022

​

26. Engineering a dynamic, controllable infectivity switch in bacteriophage T7

Chitboonthavisuk C, Luo CH, Huss P, Fernholz M, Raman S

ACS Synthetic Biology, 11, 286-296, 2022

​

25. Virus-associated organosulfur metabolism in humans and environmental systems

Kieft K, Breister AM, Huss P, Linz AM, Zanetakos E, Zhou Z, Rahlff J, Esser SP, Probst AJ, Raman S,

Roux S, Anantharaman K

Cell Reports, 36, 109471, 2021

​

24. Epistasis shapes the fitness landscape of an allosteric specificity switch

Nishikawa KK, Hoppe N, Smith R, Bingman C, Raman S

Nature Communications, 12, 5562, 2021

​

23. Mapping the functional landscape of the receptor binding domain of bacteriophage T7 by deep mutational scanning

Huss P, Meger A, Leander M, Nishikawa K, Raman S

eLife, DOI:10.7554/eLife.63775, 2021

​

22. Computation-guided design of split protein systems

Dolberg TB, Meger AT, Boucher JD, Corcoran WK, Schauer EE, Prybutok AN, Raman S*, Leonard JN*

*co-corresponding authors

Nature Chemical Biology, 17, 531-539, 2021

​

21. Functional plasticity and evolutionary adaptation of allosteric regulation

Leander M, Yuan Y, Meger AT, Cui Q, Raman S

Proceedings of the National Academy of Sciences, 117, 25445-54, 2020

​

20. Engineered bacteriophages as programmable biocontrol agents

Huss P, Raman S

Current Opinion in Biotechnology, 2019, 61, 116-121

​

19. Design of a transcriptional biosensor for the portable, on-demand detection of cyanuric acid

Liu X, Silverman AD, Alam KK, Iverson E, Lucks JB, Jewett MC, Raman S

ACS Synthetic Biology, 2020, 9, 84-94

​

18. De novo design of programmable inducible promoters

Liu X, Gupta STP, Bhimsaria D, Reed JL, Rodriguez-Martinez JA, Ansari AZ, Raman S   

Nucleic Acids Research, 2019, 47, 10452-10463

​

17. A regulatory NADH/NAD+ redox biosensor for bacteria.

Liu Y, Landick R, Raman S   

ACS Synthetic Biology, 2019, 8, 264-273

​

16. Systems Approaches to Understanding and Designing Allosteric Proteins

Raman S   

Biochemistry, 2018, 57, 376-382

​

15. What is the role of circuit design in the advancement of synthetic biology?   

Raman S 

Cell Systems, 2017, 4, 370-72

​

14. Engineering an allosteric transcription factor to respond to new ligands  

Taylor N, Garruss A, Moretti R, Chan S, Arbing M, Cascio D, Rogers J, Isaacs FJ, Kosuri S, Baker D, Fields S, Church GM, Raman S   

Nature Methods, 2016, 13, 177-82

​

13. Biosensors enable precise user-control of gene expression and real-time monitoring of intracellular metabolites

Rogers J, Guzman C, Taylor N, Raman S, Anderson K, Church GM   |   Nucleic Acids Research, 2015, 43, 7648-6082

​

12. Evolution-guided optimization of biosynthetic pathways
Raman S*, Rogers J*, Taylor N*, Church GM
Proceedings of National Academy of Sciences, 2014, 111, 17803-8
 
11. Engineering allostery
Raman S*, Taylor N*, Genuth N, Fields S, Church GM
Trends in Genetics, 2014, 30, 521-28

10. Fully automated high-quality NMR structure determination of small (2)H-enriched proteins
Tang Y, Schneider WM, Shen Y, Raman S, Inouye M, Baker D, Roth MJ, Montelione GT
J Struct Func Genomics, 2010, 11, 223-32

9. NMR structure determination for larger proteins using backbone-only data
Raman S*, Lange OF*, Rossi P, Tyka M, Wang X, Aramini J, Liu G, Ramelot TA, Eletsky A, Szyperski T, Kennedy MA, Prestegard J, Montelione GT, Baker D
Science, 2010, 327, 1014-8

8. Accurate automated protein NMR structure determination using unassigned NOESY data
Raman S, Huang YJ, Mao B, Rossi P, Aramini JM, Liu G, Montelione GT, Baker D
J Am Chem Soc, 2010, 132, 202-7

7. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8
Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K
Proteins, 2009, 77, 114-22

6. CASD-NMR: critical assessment of automated structure determination by NMR
Rosato A, Bagaria A, Baker D, Bardiaux B, Cavalli A, Doreleijers JF, Giachetti A, Guerry P, Güntert P, Herrmann T, Huang YJ, Jonker HR, Mao B, Malliavin TE, Montelione GT, Nilges M, Raman S, van der Schot G, Vranken WF, Vuister GW, Bonvin AM.
Nature Methods, 2009, 6, 625-6

5. Structure prediction for CASP8 with all-atom refinement using Rosetta
Raman S*, Vernon R*, Thompson J*, Tyka M*, Sadreyev R*, Pei J, Kim D, Kellogg E, DiMaio F, Lange O, Kinch L, Sheffler W, Kim BH, Das R, Grishin NV, Baker D.
Proteins, 2009, 77, 89-99

4. Improving NMR protein structure quality by Rosetta refinement: a molecular replacement study
Ramelot TA, Raman S, Kuzin AP, Xiao R, Ma LC, Acton TB, Hunt JF, Montelione GT, Baker D, Kennedy MA.
Proteins, 2009, 75,147-67

3. Advances in Rosetta protein structure prediction on massively parallel systems
Raman S, Qian B, Baker D, Walker RC
IBM Journal of Research and Development, 2008, 52, 7-12

2. High resolution structure prediction and the crystallographic phase problem
Qian B*, Raman S*, Das R*, Bradley P, McCoy AJ, Read RJ, Baker D
Nature, 2007, 450, 259-64

1. Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home
Das R, Qian B, Raman S, Vernon R, Thompson J, Bradley P, Khare S, Tyka MD, Bhat D, Chivian D, Kim DE, Sheffler WH, Malmström L, Wollacott AM, Wang C, Andre I, Baker D.
Proteins, 2007,69,118-28.

​

​