CompBio research
The general area of our scientific research is computational
biology, genomics, and proteomics. The goal is to elucidate processes
responsible for protein, small molecule, nucleic acid, and interactome
structure, function, interaction, evolution, and design so as to
understand (and reproduce by computing simulation) how the information
encoded by the genome of an organism specifies behaviour and
characteristics in the context of its environment.
Specific areas of ongoing research are listed below. Our research
leads us to tackle computational problems in algorithmic studies of
astronomically large spaces, bioinformatics/data mining, and massively
parallel and distributed computing. The work described in the
publications is generally encapsulated into a variety of
webservers/applications/services (links included) and downloadable software. A full
reverse chronologically ordered list of the publications is available
as part of my CV. More significant publications
are denoted by * along with other annotations such as an accompanying
cover or introductory article signifying the notability of a
publication so that people may use this as a guide to help focus their
studies.
Structural and functional studies of biologically important
proteins, systems, and problems. Use the structure and function
prediction tools developed by us to help guide experimentalists in
manipulating proteins and extracting information about their function
and structure in vivo, both at the single molecule as well as
at the genomic/systems levels. Some key areas include work on
therapeutic (inhibitor) discovery and nanobiotechnology. This work is
usually done in collaboration with experimentalists. I list these
papers first since they demonstrate a true application of the work we
do. In many cases, these are prospective verification (i.e.,
a prediction is made before the answer is known and verified).
- Mangione W, Falls Z, Samudrala R. Effective
holistic characterization of small molecule effects using
heterogeneous biological networks. Frontiers in
Pharmacolology 14: 1113007, 2023.
- Kumari R, Sharma SD, Kumar A, Ende Z, Mishina M, Wang Y, Falls
Z, Samudrala R, Pohl J, Knight PR, Sambhara S. Antiviral
approaches against influenza virus. Clinical Microbiology
Reviews 36: e0004022, 2023.
- Bruggemann L, Falls Z, Mangione W, Schwartz SA, Battaglia S,
Aalinkeel R, Mahajan SD, Samudrala R. Multiscale
analysis and validation of effective drug combinations targeting
driver KRAS mutations in non-small cell lung cancer.
International Journal of Molecular Sciences 24: 997,
2023. *
- Mangione W, Falls Z, Samudrala R. Optimal
COVID-19 therapeutic candidate discovery using the CANDO
platform. Frontiers in Pharmacology 13: 970494,
2022. *
- Moukheiber L, Mangione W, Moukheiber M, Maleki S, Falls Z, Gao
M, Samudrala R. Identifying
protein features and pathways responsible for toxicity using machine
learning and tox21: Implications for predictive
toxicology. Molecules 27: 3021, 2022. *
- Mammen MJ, Tu C, Morris MC, Richman S, Mangione W, Falls Z, Qu
J, Broderick G, Sethi S, Samudrala R. Proteomic
network analysis of bronchoalveolar lavage fluid in ex-smokers to
discover implicated protein targets and novel drug treatments for
chronic obstructive pulmonary
disease. Pharmaceuticals 15: 566, 2022. *
- Falls Z, Fine J, Chopra G, Samudrala R. Accurate
prediction of inhibitor binding to HIV-1 protease using
CANDOCK. Frontiers in Chemistry 9: 775513, 2022.
- Schuler J, Falls Z, Mangione W, Hudson M, Bruggemann L,
Samudrala R. Evaluating
performance of drug repurposing technologies. Drug
Discovery Today 27: 49-64, 2022. *
- Overhoff B, Falls Z, Mangione W, Samudrala R. A
deep-learning proteomic-scale approach for drug
design. Pharmaceuticals (Basel) 14: 1277, 2021. *
- Dey-Rao R, Smith GR, Timilsina U, Falls Z, Samudrala R,
Stavrou S, Melendy T. A
fluorescence-based, gain-of-signal, live cell system to evaluate
SARS-CoV-2 main protease inhibition. Antiviral
Research 195: 105183, 2021.
- Palanikumar L, Karpauskaite L, Al-Sayegh M, Chehade I, Alam M,
Hassan S, Maity D, Ali L, Kalmouni M, Hunashal Y, Ahmed J, Houhou T,
Karapetyan S, Falls Z, Samudrala R, Pasricha R, Esposito G,
Afzal AJ, Hamilton AD, Kumar S, Magzoub M. Protein
mimetic amyloid inhibitor potently abrogates cancer-associated
mutant p53 aggregation and restores tumor suppressor
function. Nature Communications 12: 3962, 2021.
- Hudson ML, Samudrala R. Multiscale
virtual screening optimization for shotgun drug repurposing using
the CANDO platform. Molecules 26: 2581-2597, 2021.
- Chatrikhi R, Feeney CF, Pulvino MJ, Alachouzos G, MacRae AJ,
Falls Z, Rai S, Brennessel WW, Jenkins JL, Walter MJ, Graubert TA,
Samudrala R, Jurica MS, Frontier AJ, Kielkopf CL. A
synthetic small molecule stalls pre-mRNA splicing by promoting an
early-stage U2AF2-RNA complex. Cell Chemical Biology
28: 1145-1157, 2021.
- Mangione W, Falls Z, Chopra G, Samudrala R. cando.py:
Open source software for predictive bioanalytics of large scale
drug-protein-disease data. Journal of Chemical Information
and Modeling 60: 4131-4136, 2020. *
- Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R.
Shotgun
drug repurposing biotechnology to tackle epidemics and
pandemics. Drug Discovery Today 25: 1126-1128,
2020. *
- Fine J, Konc J, Samudrala R, Chopra G. CANDOCK:
Chemical Atomic Network-Based Hierarchical Flexible Docking
Algorithm Using Generalized Statistical
Potentials. Journal of Chemical Information and
Modeling 60: 1509-1527, 2020. *
- Fine J, Lackner R, Samudrala R, Chopra G. Computational
chemoproteomics to understand the role of selected psychoactives in
treating mental health indications. Scientific
Reports 9, 1315, 2019. *
- Schuler J, Samudrala R. Fingerprinting
CANDO: Increased accuracy with structure and ligand based shotgun
drug repurposing. ACS Omega 4: 17393-17403, 2019.
*
- Schuler J, Mangione W, Samudrala R, Ceusters W. Foundations
for a realism-based drug repurposing ontology. Proceedings of
the 10th International Conference on Biomedical Ontology, 2019.
- Falls Z, Mangione W, Schuler J, Samudrala R. Exploration
of interaction scoring criteria in the CANDO platform. BMC
Research Notes 12: 318, 2019. *
- Mangione W, Samudrala R. Identifying protein features
responsible for improved drug repurposing accuracies using the CANDO
platform: Implications for drug design. Molecules 24: 167,
2019. *
- Schuler J, Hudson M, Schwartz D, Samudrala R. A
systematic review of computational drug discovery, development, and
repurposing for Ebola Virus Disease treatment. Molecules
22: E1777, 2017.
- Chopra C, Kaushik S, Elkin PL, Samudrala R. Combating
Ebola with repurposed therapeutics using the CANDO
platform. Molecules 21: 1537, 2016. *
- Craig JK, Risler JK, Loesch KA, Dong W, Baker D, Barrett LK,
Subramanian S, Samudrala R, Van Voorhis WC. Mycobacterium
cytidylate kinase appears to be an undruggable target. Journal
of Biomolecular Design 21: 695-700, 2016.
- Chopra G, Samudrala R. Exploring polypharmacology in
drug discovery and repurposing using the CANDO
platform. Current Pharmaceutical Design 22: 3109-3123
2016.
- Manocheewa S, Mittler JE, Samudrala R, Mullins
JI. Composite sequence-structure stability models as screening tools
for identifying vulnerable targets for HIV drug and vaccine
development. Viruses 7: 5718-5735, 2015.
- Sethi G, Chopra G, Samudrala R. Multiscale
modelling of relationships between protein classes and drug behavior
across all diseases using the CANDO platform. Mini Reviews
in Medicinal Chemistry, 15: 705-717, 2015.
- Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K,
Samudrala R. CANDO and
the infinite drug discovery frontier. Drug Discovery
Today 19: 1353-1363, 2014. *
- Lertkiatmongkol P, Assawamakin A, White G, Chopra G,
Rongnoparut P, Samudrala R, Tongsima S. Distal effect of
amino acid substitutions in CYP2C9 polymorphic variants causes
differences in interatomic interactions against
(S)-warfarin. PLoS One 8: e74053, 2013.
- Strategic protein target analysis for developing drugs to stop
dental caries. Horst JA, Pieper U, Sali A, Zhan L, Chopra G,
Samudrala R, Featherstone JD. Advances in Dental
Research 24: 86-93, 2012. *
- Horst JA, Laurenzi A, Bernard B, Samudrala R. Computational
multitarget drug discovery. Polypharmacology
263-301, 2012. *
- Nicholson CO, Costin JM, Rowe DK, Lin L, Jenwitheesuk E,
Samudrala R, Isern S, Michael SF. Viral
entry inhibitors block dengue antibody-dependent enhancement in
vitro. Antiviral Research 89: 71-74 2010. *
- Movahedzadeh F, Balaubramanian V, Bernard B,
Iyer S, Samudrala R, Franzblau SG, Balganesh TS.
Anti-tuberculosis agents: A rational approach for discovery and
development. Genomic and computational tools for emerging
infectious diseases, 2010.
- Costin JM, Jenwitheesuk E, Lok S-M, Hunsperger E, Conrads KA,
Fontaine KA, Rees CR, Rossmann MG, Isern S, Samudrala R,
Michael SF. Structural optimization and de novo design of
dengue virus entry inhibitory peptides. PLoS Neglected Tropical
Diseases 4: e721, 2010. *
- Bernard B, Samudrala R. A
generalized knowledge-based discriminatory function for biomolecular
interactions. Proteins: Structure, Function, and
Bioinformatics 76: 115-128, 2009.
- Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala
R. Novel paradigms
for drug discovery: Computational multitarget
screening. Trends in Pharmacological Sciences 29:
62-71, 2008. [Accompanying cover.] *
- Samudrala R, Jenwitheesuk E. Identification of potential HIV-1
targets of minocycline. Bioinformatics 23:
2797-2799, 2007.
- Wang K, Mittler J, Samudrala R. Comment on "Evidence for
positive epistatis in HIV-1". Science 312: 848b,
2006.
- Jenwitheesuk E, Samudrala R. Identification of potential
multitarget antimalarial drugs. Journal of the American
Medical Association 294: 1490-1491, 2005. *
- Jenwitheesuk E, Samudrala R. Heptad-repeat-2 mutations
enhance the stability of the enfuvirtide-resistant HIV-1 gp41
hairpin structure. Antiviral Therapy 10: 893-900,
2005. *
- Jenwitheesuk E, Wang K, Mittler J, Samudrala R.
PIRSpred: A webserver for
reliable HIV-1 protein-inhibitor resistance/susceptibility
prediction. Trends in Microbiology 13: 150-151,
2005.
- Jenwitheesuk E, Samudrala R. Virtual screening of HIV-1
protease inhibitors against human cytomegalovirus protease using
docking and molecular dynamics. AIDS 19: 529-533,
2005.
- Jenwitheesuk E, Samudrala R. Prediction of HIV-1
protease inhibitor resistance using a protein-inhibitor flexible
docking approach. Antiviral Therapy 10: 157-166,
2005.
- Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Improved accuracy of HIV-1
genotypic susceptibility interpretation using a consensus
approach. AIDS 18: 1858-1859, 2004.
- Jenwitheesuk E, Samudrala R. Identifying inhibitors of
the SARS coronavirus proteinase. Bioorganic & Medicinal
Chemistry Letters 13: 3989-3992, 2003. [Most Cited Paper
2003 - 2006 Award.] *
- Jenwitheesuk E, Samudrala R. Improved prediction of
HIV-1 protease-inhibitor binding energies by molecular dynamics
simulations. BMC Structural Biology 3: 2, 2003. *
- Wang K, Jenwitheesuk E, Samudrala R, Mittler J. Simple linear model
provides highly accurate genotypic predictions of HIV-1 drug
resistance. Antiviral Therapy 9: 343-352, 2004.
- Wang K, Samudrala R, Mittler J. Weak
agreement between predictions of ``reduced susceptibility'' from
Antivirogram and PhenoSense assays. Journal of Clinical
Microbiology 42: 2353-2354, 2004.
- Wang K, Samudrala R, Mittler J. HIV-1
genotypic drug resistance interpretation algorithms need to include
hypersusceptibility mutations. Journal of Infectious
Diseases 190: 2055-2056, 2004.
- Wang K, Samudrala R, Mittler J. Antivirogram
or PhenoSense: a comparison of their reproducibility and an analysis
of their correlation. Antiviral Therapy 9: 703-712,
2004.
- Protein inhibitor
resistance/susceptibility prediction (PIRSpred) web server
module
- Computational
analysis of novel drug opportunities (CANDO) platform
Nanobiotechnology
- Cementomimetics-constructing a cementum-like biomineralized
microlayer via amelogenin-derived peptides. Gungormus M, Oren EE,
Horst JA, Fong H, Hnilova M, Somerman MJ, Snead ML, Samudrala
R, Tamerler C, Sarikaya M. International Journal of Oral
Sciences 2: 69-77, 2012. *
- Notman R, Oren EE, Tamerler C, Sarikaya M, Samudrala R,
Walsh TR. Solution study of engineered quartz binding
peptides using replica exchange molecular dynamics.
Biomacromolecules 11: 3266-3274, 2010.
- Oren EE, Notman R, Kim IW, Evans J, Walsh T, Samudrala
R, Tamerer C, Sarikaya M. Probing the molecular mechanisms of
quartz-binding peptides. Langmuir 26: 11003-11009,
2010.
- Samudrala R, Oren EE, Cheng C, Horst, J, Bernard B,
Gungormus M, Hnilova M, Fong H, Tamerler C, Sarikaya M. Knowledge-based design of
inorganic binding peptides. Proceedings of the conference
on the Foundations of Nanoscience: Self-Assembled Architectures and
Devices, 2008.
- Evans JS, Samudrala R, Walsh TR, Oren EE, Tamerler
C. Molecular design of
inorganic-binding polypeptides. MRS Bulletin 33:
514-518, 2008. [Accompanying
cover and introductory article with biographies on pages 504-512.] *
- Oren EE, Tamerler C, Sahin D, Hnilova M, Seker UOS, Sarikaya M,
Samudrala R. A novel
knowledge-based approach for designing inorganic binding
peptides. Bioinformatics 23: 2816-2822, 2007. *
General and specific functional studies
- Bruggemann L, Hawthorne C, Samudrala R, Lopez-Campos
GH. Linking genome and exposome: Computational analysis of human
variation in chemical-target interactions. Student Health
Technology Informatics 270: 1331-1332, 2020.
- Mandloi S, Falls Z, Deng R, Samudrala R, Elkin
PL. Association of C>U RNA editing with human disease
variants. Student Health Technology Informatics 270:
1205-1206, 2020.
- Homo-dimerization and ligand binding by the leucine-rich repeat
domain at RHG1/RFS2 underlying resistance to two soybean pathogens.
Afzal AJ, Srour A, Goil A, Vasudaven S, Liu T, Samudrala R,
Dogra N, Kohli P, Malakar A, Lightfoot DA. BMC Plant
Biology 13: 43, 2013.
- Self-assembly of filamentous amelogenin requires calcium and
phosphate: from dimers via nanoribbons to fibrils. Martinez-Avila
O, Wu S, Kim SJ, Cheng Y, Khan F, Samudrala R, Sali A, Horst
JA, Habelitz S. Biomacromolecules 13: 3494-502, 2012.
- An P, Li R, Wang JM, Yoshimura T, Takahashi M, Samudrala
R, O'Brien SJ, Phair J, Goedert JJ, Kirk GD, Troyer JL, Sezgin
E, Buchbinder SP, Donfield S, Nelson GW, Winkler CA. Role of exonic
variation in chemokine receptor genes on AIDS: CCRL2 F167Y
association with pneumocystis pneumonia. PLoS Genetics
7: e1002328, 2011.
- Horst OV, Horst JA, Samudrala R, Dale BA. Caries
induced cytokine network in the odontoblast layer of human teeth.
BMC Immunology 12: 9, 2011.
- Cunningham ML, Horst JA, Rieder MJ, Hing AV, Stanaway IB, Park
SS, Samudrala R, Speltz ML. IGF1R variants associated with
isolated single suture craniosynostosis. The American Journal
of Human Genetics 155A: 91-97, 2011. [Accompanying cover.]
- Borlee BR, Goldman AD, Murakami K, Samudrala R, Wozniak
DJ, Parsek MR. Pseudomonas aeruginosa uses
a cyclic-di-GMP-regulated adhesin to reinforce the biofilm
extracellular matrix. Molecular Microbiology 75:
827-842, 2010. [Accompanying cover.]
- Goldman AD, Leigh JA, Samudrala R. Comprehensive computational
analysis of Hmd enzymes and paralogs in methanogenic
Archaea. BMC Evolutionary Biology 9: 199, 2009.
- Jenkins C, Samudrala R, Geary S, Djordjevic SP.
Structural and functional
characterisation of an organic hydroperoxide resistance (Ohr)
protein from Mycoplasma gallisepticum.
Journal of Bacteriology 190: 2206-2208, 2008.
- Chevance FFV, Takahashi N, Karlinsey JE, Gnerer J, Hirano T,
Samudrala R, Aizawa S-I, Hughes KT. The mechanism of outer
membrane penetration by the eubacterial flagellum and implications
for spirochete evolution. Genes and Development 21:
2326-2335, 2007.
- Bockhorst J, Lu F, Janes JH, Keebler J, Gamain B, Awadalla P, Su
X, Samudrala R, Jojic N, Smith JD. Structural polymorphism
and diversifying selection on the pregnancy malaria vaccine
candidate VAR2CSA. Molecular and Biochemical
Parasitology 155: 103-112, 2007.
- Berube PM, Samudrala R, Stahl DA. Transcription of
amoC is associated with the recovery of Nitrosomonas
europaea from ammonia starvation. Journal of
Bacteriology 89: 3935-3944, 2007.
- Korotkova N, Le Trong I, Samudrala R, Korotkov K, Van
Loy CP, Bui A-L, Moseley SL, Stenkamp RE. Crystal structure and mutational
analysis of the DaaE adhesin of Escherichia
coli. Journal of Biological Chemistry 281:
22367-22377, 2006.
- Howell DPG, Samudrala R, Smith JD. Disguising itself -
insights into Plasmodium falciparum binding and immune
evasion from the DBL crystal structure. Molecular and
Biochemical Parasitology 148: 1-9, 2006.
- Wang W, Zheng H, Yang S, Yu H, Li J, Jiang H, Su J, Yang L,
Zhang J, McDermott J, Samudrala R, Wang J, Yang H, Yu J,
Kristiansen K, Wong GK, Wang J. Origin and evolution of new exons in
rodents. Genome Research 15: 1258-1264, 2005.
- Liu T, Jenwitheesuk E, Teller D, Samudrala R.
Structural insights into the Cellular
Retinaldehyde Binding Protein (CRALBP). Proteins:
Structure, Function, and Bioinformatics 61: 412-422, 2005.
- Ekwa-Ekok C, Diaza GA, Carlson C, Hasegawad T,
Samudrala R, Limf K, Yabug JM, Levya B, Schnapp LM. Genomic organization and sequence
variation of the human integrin subunit 8 gene
(ITGA8). Matrix Biology 23: 487-496, 2004.
- Wang J, Zhang J, Zheng H, Li J, Liu D, Li H, Samudrala
R, Yu J, Wong GK. Mouse
transcriptome: Neutral evolution of "non-coding" complementary
DNAs. Nature 431, 2004.
- Jenkins C, Samudrala R, Anderson I, Hedlund BP, Petroni
G, Michailova N, Pinel N, Overbeek R, Rosati G, Staley JT. Genes for the cytoskeletal protein
tubulin in the bacteria genus
Prosthecobacter. Proceedings of the
National Academy of Sciences 99: 17049-17054, 2002.
- Van Loy CP, Sokurenko EP, Samudrala R, Moseley S.
Identification of a DAF binding
domain in the Dr adhesin. Molecular Microbiology
45: 439-452, 2002.
- Samudrala R, Xia Y, Levitt M, Cotton NJ, Huang ES, Davis R.
Probing structure-function
relationships of the DNA polymerase alpha-associated zinc-finger
protein using computational approaches. In Altman R, Dunker K,
Hunter L, Klein T, Lauderdale K, eds. Proceedings of the
Pacific Symposium on Biocomputing 179-189, 2000.
- Protinfo structure, function, and interaction prediction server
We use our prediction protocols to explore early evolution and
origin of life issues.
- Goldman AD, Barrows J, Samudrala R. The enzymatic and metabolic
capabilities of early life. PLoS One 7: e39912,
2012. *
- Goldman AD, Horst JA, Hung L-H, Samudrala R. Evolution
of the protein repertoire. Systems Biology: 207-237,
2012. (R Meyers, Editor. Wiley-VCH Wienheim, Germany.)
- Goldman AD, Samudrala R, Barrows J. The evolution and
functional repertoire of translation proteins following the origin of
life. Biology Direct 5: 15, 2010. *
- Goldman AD, Leigh JA, Samudrala R. Comprehensive computational
analysis of Hmd enzymes and paralogs in methanogenic
Archaea. BMC Evolutionary Biology 9: 199, 2009.
Application and integration of single molecule structure and
function prediction techniques to whole genomes and proteomes in an
integrated manner. Combine single molecule and genomic/proteomic data
to to explore the relationships among the molecular and organismal
(systems) worlds and create a comprehensive picture of the
relationship between genotype and phenotype.
- Hung L-H, Samudrala R. Rice protein models from the
Nutritious Rice for the World Project. bioRxiv 091975; doi: https://doi.org/10.1101/091975,
2016.
- Minie M. Samudrala R. The promise and challenge of
digital biology. Journal of Bioengineering and Biomedical
Sciences 3: e118, 2013. editorial.
- Matasci N, Hung L-H, ..., Samudrala R, Tian Z, Wu X, Sun
X, Zhang Y, Wang J, Leebens-Mack J, Wong GSK. Data access for the
1,000 Plants (1KP) project. Gigascience 3: 17, 2014.
- McDermott J, Ireton R, Montgomery K, Bumgarner R, Samudrala
R (editors). Computational
systems biology. Methods in Molecular Biology 541:
v-ix, 2009. *
- Frazier Z, McDermott J, Samudrala R. Computational representation of
biological systems. Methods in Molecular Biology
541: 535-549, 2009.
- Guerquin M, McDermott J, Samudrala R. The Bioverse API and Web
Application. Methods in Molecular Biology 541:
511-534, 2009.
- Rashid I, McDermott J, Samudrala R. Inferring molecular interaction
pathways from eQTL data. Methods in Molecular
Biology 541: 211-223, 2009.
- Wichadakul D, McDermott J, Samudrala R. Prediction and
integration of regulatory and protein-protein
interactions. Methods in Molecular Biology 541:
101-143, 2009.
- McDermott J, Wang J, Yu J, Wong GSK,
Samudrala R. In Rao GP, Wagner C, Singh RK,
editors. Prediction and
annotation of plant protein interaction
networks. Application of Genomics and Bioinformatics in
Plants (Studium Press) 207-238, 2008.
- McDermott J, Samudrala R. Bioinformatic characterization
of plant networks. Proceedings of the Asia Pacific Conference
on Plant Tissue Culture and Agrobiotechnology, 2007.
- Chang AN, McDermott J, Guerquin M, Frazier Z, Samudrala
R. Integrator: Interactive
graphical search of large protein
interactomes over the Web. BMC Bioinformatics 7:
146, 2006.
- McDermott J, Bumgarner RE, Samudrala R. Functional annotation from
predicted protein interaction networks.
Bioinformatics 21: 3217-3226, 2005. *
- McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R.
BIOVERSE: Enhancements to the
framework for structural, functional, and contextual annotations of
proteins and proteomes. Nucleic Acids Research
33: W324-W325, 2005. *
- Chang AN, McDermott J, Samudrala R.
An enhanced java
graph applet interface for visualizing
interactomes. Bioinformatics 21: 1741-1742, 2005.
- Yu J, Wang J, Lin W, Li S, Li H, Zhou J, ..., McDermott J,
Samudrala R, Wang J, Wong GK. The genomes of Oryza
sativa: A history of duplications. PLoS
Biology 3: e38, 2005. *
- McDermott J, Samudrala R. Enhanced functional information from
protein networks. Trends in
Biotechnology 22: 60-62, 2004. *
- McDermott J, Samudrala R. BIOVERSE: Functional, structural,
and contextual annotation of proteins and
proteomes. Nucleic Acids Research 31:
3736-3737, 2003. *
- McDermott J, Samudrala R. The Bioverse: An object-oriented
genomic database and webserver written in
Python. In Proceedings of the conference on Objects in
Bio- & Chem-Informatics, 2002.
- Bioverse framework
- Protinfo structure, function, and interaction prediction server
Methods for predicting interactions between molecules.
- Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R.
Protinfo PPC: A web
server for atomic level prediction of protein complexes.
Nucleic Acids Research 37: W519-W525, 2009. *
- Bernard B, Samudrala R. A
generalized knowledge-based discriminatory function for biomolecular
interactions. Proteins: Structure, Function, and
Bioinformatics 76: 115-128, 2009. *
- McDermott J, Bumgarner RE, Samudrala R. Functional annotation from
predicted protein interaction networks.
Bioinformatics 21: 3217-3226, 2005. *
- McDermott J, Samudrala R. Enhanced functional information from
protein networks. Trends in
Biotechnology 22: 60-62, 2004.
- Bioverse framework
Generally applicable methods for predicting protein function from
sequence and/or structure.
- McDermott JE, Corrigan A, Peterson E, Oehmen C,
Niemann G, Cambronne ED, Sharp D, Adkins JN, Samudrala R,
Heffron F. Computational prediction of type III and IV secreted
effectors in Gram-negative bacteria. Infection and Immunity
79: 23-32, 2010.
- Horst JA, Wang K, Horst OV, Cunningham ML, Samudrala
R. Disease risk of
missense mutations using structural inference from predicted
function. Current Protein & Peptide Science 11:
573-588, 2010.
- Horst J, Samudrala R. A protein sequence meta-functional
signature for calcium binding residue prediction. Pattern
Recognition Letters 31: 2103-2112, 2010. *
- Samudrala R, Heffron F, McDermott J. In silico identification of
secreted effectors in Salmonella typhimurium. PLoS
Pathogens 5: e1000375, 2009. *
- Wang K, Horst J, Cheng G, Nickle D, Samudrala R. Protein meta-functional signatures
from combining sequence, structure, evolution and amino acid
property information. PLoS Computational Biology 4:
e1000181, 2008. *
- Wang K, Samudrala R. Incorporating background frequency
improves entropy-based residue conservation measures. BMC
Bioinformatics 7: 385, 2006.
- Wang K, Samudrala R. Automated functional
classification of experimental and predicted protein
structures. BMC Bioinformatics 7: 278, 2006. *
- Cheng G, Qian B, Samudrala R, Baker D. Improvement in
protein functional site prediction by distinguishing structural and
functional constraints on protein family evolution using
computational design. Nucleic Acids Research
33: 5861-5867, 2005. *
- McDermott J, Bumgarner RE, Samudrala R. Functional annotation from
predicted protein interaction networks.
Bioinformatics 21: 3217-3226, 2005. *
- Wang K, Samudrala R. FSSA: A novel method for
identifying functional signatures from structural
alignments. Bioinformatics 21: 2969-2977, 2005. *
- McDermott J, Samudrala R. Enhanced functional information from
protein networks. Trends in Biotechnology 22: 60-62, 2004.
- Protinfo structure, function, and interaction prediction server
De novo protein structure prediction
The basic paradigm is to sample the conformational space
exhaustively or semi-exhaustively such that native-like conformations
are observed. These conformations are selected using the all-atom
based scoring functions. Some methods have had good success in the
CASP blind prediction experiments.
- Laurenzi A, Hung L-H, Samudrala R. Structure prediction
of partial length protein sequences: applications in foldability
prediction and EST annotation. International Journal of
Molecular Sciences 214: 14892-14907, 2013.
- Liu T, Horst J, Samudrala R. A novel method for
predicting and using distance constraints of high accuracy for
refining protein structure prediction. Proteins: Structure,
Function, and Bioinformatics 77: 220-234, 2009. *
- Horst J, Samudrala R. Diversity of protein structures
and difficulties in fold recognition: The curious case of Protein
G. F1000 Biology Reports 1:69, 2009. *
- Hung L-H, Ngan S-C, Samudrala R. De novo
protein structure prediction. In Xu Y, Xu D, Liang J, editors.
Computational Methods for Protein Structure Prediction and
Modeling 2: 43-64, 2007.
- Hung L-H, Ngan S-C, Liu T, Samudrala R.
PROTINFO: New algorithms for
enhanced protein structure prediction. Nucleic Acids
Research 33: W77-W80, 2005. *
- Hung L-H, Samudrala R. PROTINFO: Secondary and
tertiary protein structure prediction. Nucleic Acids
Research 31: 3296-3299, 2003. *
- Samudrala R, Levitt M. A comprehensive analysis of 40
blind protein structure predictions. BMC Structural
Biology 2: 3-18, 2002. *
- Samudrala R. Lessons from blind protein structure
prediction experiments. In Grohima M, Selvaraj S,
eds. Recent Research Developments in Protein Folding,
Stability, and Design, 123-139, 2002.
- Xia Y, Huang ES, Levitt M, Samudrala R. Ab initio
construction of protein tertiary structures using a hierarchical
approach. Journal of Molecular Biology, 300:
171-185, 2000. *
- Samudrala R, Xia Y, Levitt M. Huang ES. Ab initio prediction of
protein structure using a combined hierarchical
approach. Proteins: Structure, Function, and Genetics
S3: 194-198, 1999. *
- Huang ES, Samudrala R, Ponder JW. Ab initio protein
structure prediction results using a simple distance geometry
method. unpublished.
- Huang ES, Samudrala R, Ponder JW. Ab initio fold
prediction of small helical proteins using distance geometry and
knowledge-based scoring functions. Journal of Molecular
Biology 290:267-281, 1999.
- Huang ES, Samudrala R, Ponder JW. Distance geometry generates native-like
folds for small helical proteins using the consensus distances of
predicted protein structures. Protein Science 7:
1998-2003, 1998.
- Samudrala R, Xia Y, Levitt M, Huang ES. A combined approach for ab
initio construction of low resolution protein tertiary
structures from sequence. In Altman R, Dunker K, Hunter L,
Klein T, Lauderdale K, eds. Proceedings of the Pacific
Symposium on Biocomputing 505-516, 1999.
- Protinfo structure, function, and interaction prediction server
Comparative modelling of protein structure
Handling the problem of context sensitivity in protein
structures. Some methods have had good success in the CASP blind
prediction experiments.
- Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf
NM, Samudrala R, Mehboob S, Dementiev A, Abad-Zapatero C,
Movahedzadeh F. Rv0100, a proposed acyl carrier protein in
Mycobacterium tuberculosis: expression, purification and
crystallization. Corrigendum. Acta Crystallograpica F
Structural Biology Communications 76: 192-193, 2020.
- Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf
NM, Samudrala R, Mehboob S, Movahedzadeh F. Rv0100, a
proposed acyl carrier protein in Mycobacterium tuberculosis:
expression, purification and crystallization. Acta
Crystallograpica F Structural Biology Communications. 75:
646-651, 2019.
- Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R.
Protinfo PPC: A web server for
atomic level prediction of protein complexes. Nucleic
Acids Research 37: W519-W525, 2009.
- Liu T, Horst J, Samudrala R. A novel method for
predicting and using distance constraints of high accuracy for
refining protein structure prediction. Proteins: Structure,
Function, and Bioinformatics 77: 220-234, 2009.
- Liu T, Guerquin M, Samudrala R. Improving the accuracy of
template-based predictions by mixing and matching between initial
models. BMC Structural Biology 8: 24, 2008.
- Hung L-H, Ngan S-C, Liu T, Samudrala R.
PROTINFO: New algorithms for
enhanced protein structure prediction. Nucleic Acids
Research 33: W77-W80, 2005.
- Hung L-H, Samudrala R. PROTINFO: Secondary and
tertiary protein structure prediction. Nucleic Acids
Research 31: 3296-3299, 2003. *
- Samudrala R, Levitt M. A comprehensive analysis of 40
blind protein structure predictions. BMC Structural
Biology 2: 3-18, 2002. *
- Samudrala R. Lessons from blind protein structure
prediction experiments. In Grohima M, Selvaraj S,
eds. Recent Research Developments in Protein Folding,
Stability, and Design, 123-139, 2002.
- Samudrala R, Moult J. A graph-theoretic algorithm for
comparative modelling of protein structure. Journal of
Molecular Biology 279: 287-302, 1998. *
- Samudrala R, Moult J. Handling context-sensitivity
in protein structures using graph theory: bona fide
prediction Proteins: Structure, Function, and
Genetics 29S: 43-49, 1997. *
- Samudrala R. A
graph-theoretic solution to the context-sensitivity problem in
protein structure prediction. Ph.D. thesis, 1997.
- Samudrala R, Pedersen JT, Zhou H, Luo R, Fidelis K,
Moult J. Confronting the
problem of interconnected structural changes in the comparative
modelling of proteins. Proteins: Structure, Function, and
Genetics 23: 327-336, 1995.
- Protinfo structure, function, and interaction prediction server
Protein structure from combining theory and experiment
Use the structure prediction methods described below with
experimental data to produce better results.
- Hung L-H, Samudrala R. An automated assignment-free
Bayesian approach for accurately identifying proton contacts from
NOESY data. Journal of Biomolecular NMR 36:
189-198, 2006. *
- Hung L-H, Samudrala R. PROTINFO: Secondary and
tertiary protein structure prediction. Nucleic Acids
Research 31: 3296-3299, 2003. *
- Hung L-H, Samudrala R. Accurate and automated
classification of protein secondary structure with
PsiCSI. Protein Science 12: 288-295, 2003. *
- Protinfo structure, function, and interaction prediction server
Scoring/discriminatory functions for protein structure prediction
We primarily use an all-atom distance dependent conditional
probability discriminatory function that is surprisingly accurate at
selecting correct from incorrect protein conformations. It is used
both for ab initio prediction and comparative modelling. We
also use a number of other scoring functions as filters, and also
develop databases of incorrect conformations ("decoys") to help
evaluate scoring functions.
- Moughon S, Samudrala R. LoCo: a new backbone-only
scoring function for protein structure prediction. BMC
Bioinformatics 12: 368, 2011.
- Bernard B, Samudrala R. A
generalized knowledge-based discriminatory function for biomolecular
interactions. Proteins: Structure, Function, and
Bioinformatics 76: 115-128, 2009.
- Ngan S-C, Hung L-H, Liu T, Samudrala R. Scoring functions for de
novo protein structure prediction revisited. Methods
in Molecular Biology 413: 243-282, 2007.
- Liu T, Samudrala R. The effect of experimental
resolution on the performance of knowledge-based discriminatory
functions for protein structure selection. Protein
Engineering, Design and Selection 19: 431-437, 2006.
- Ngan S-C, Inouye M, Samudrala R. A knowledge-based
scoring function based on residue triplets for protein structure
prediction. Protein Engineering, Design and
Selection 19: 187-193, 2006.
- Wang K, Fain B, Levitt M, Samudrala R. Improved protein structure
selection using decoy-dependent discriminatory
functions. BMC Structural Biology 4: 8, 2004. *
- Samudrala R, Levitt M. Decoys 'R' Us: A database of
incorrect protein
conformations for evaluating scoring functions. Protein
Science, 9: 1399-1401, 2000.
- Huang ES, Samudrala R, Park BH. Scoring functions
for ab initio folding. In Walker J, Webster D,
eds. Predicting Protein Structure: Methods and Protocols
Humana Press, 2000.
- Samudrala R, Moult J. An all-atom distance-dependent
conditional probability discriminatory function for protein
structure prediction. Journal of Molecular Biology
275: 893-914, 1998. *
- Decoys 'R' Us database
Side chain prediction
There are two papers in this area. The first is a work on exactly
what it is that primarily determines side chain conformational
preferences in proteins. The main thrust here is the use of the
discriminatory function to select the most probable side chain
rotamers given a large number of possible conformations. The second
paper compares different methods for side chain prediction.
- Samudrala R, Huang ES, Koehl P, Levitt M. Side chain construction on
non-native main chains using an all-atom discriminatory
function. Protein Engineering, 7: 453-457, 2000.
- Samudrala R, Moult J. Determinants of side chain
conformational preferences in protein
structures. Protein Engineering 11: 991-997, 1998.
We prefer to make our clusters from cheap components that can be
readily discarded, and prefer to completely decentralise our
systems. Also included in this category are algorithms developed to
handle the scientific problems we face.
- Hung L-H, Samudrala R. fast_protein_cluster: parallel and
optimized clustering of large scale protein modeling
data. Bioinformatics 30: 1774-1776, 2014.
- Hung L-H, Samudrala R. Accelerated protein structure
comparison using TM-score-GPU. Bioinformatics 28:
2191-2192, 2012.
- Hung LH, Guerquin M, Samudrala R. GPU-Q-J, a fast method
for calculating root mean square deviation (RMSD) after optimal
superposition. BMC Research Notes 4: 97, 2011.
- Frazier Z, McDermott J, Samudrala R. Computational representation of
biological systems. Methods in Molecular Biology
541: 535-549, 2009.
- Guerquin M, McDermott J, Samudrala R. The Bioverse API and Web
Application. Methods in Molecular Biology 541:
511-534, 2009.
- Samudrala R. Taking the
cost out of firewalls. LinuxWorld Magazine 1: 58-59, 2003.
- Samudrala R. Linux
Cluster HOWTO, 2003.
- McDermott J, Samudrala R. The Bioverse: An object-oriented
genomic database and webserver written in
Python. In Proceedings of the conference on Objects in
Bio- & Chem-Informatics, 2002.
- Samudrala R. Installing and
using RAID. In Danesh A, Gautam D, eds. Special Edition
Using Linux System Administration, Que Publishing, 2000.
Samudrala Computational Biology Research Group (CompBio) ||
Ram Samudrala
|| me@ram.org