Molecular basis of receptor-ligand recognition in the Immunological Synapse

Our long-term goal is to understand the principles underlying molecular recognition and selectivity at the immunological synapse. This includes both the identification of novel cognate receptor ligand intercations and to use the gained knowledge to realize surgically defined mutants with altered affinities and selectivities, which can act as biologic drugs

Modeling protein structures, designing novel folds

We are developing a computational approach to model proteins for which a limited number of experimental restraints are available. We utilize our recently developed fragment library of supersecondary structure elements (Smotifs) that was shown to have saturated almost 20 years ago. We hypothesize that all protein folds should be possible to build from this library. We are developing algorithms that take advantage of NMR chemical shift information to identify a subset of Smotifs that form a protein and setting up optimization approaches that will rapidly assemble overlapping Smotifs into compact folds.

Evolution of robustness in gene networks (Protein-DNA interactions, structure based prediction of DNA binding motifs.)

Previous research has shown gene regulatory networks are robust to perturbations at the level of the connections between transcription factors. We investigate the mechanisms underlying the evolution of robustness in gene networks using a modeling approach, which considers three levels: binding of individual transcription factors to DNA, dynamics of gene expression levels, and fitness effects at the population level.

Gil N, Shrestha R, Fiser A
Estimating the accuracy of pharmacophore-based detection of cognate receptor-ligand pairs in the immunoglobulin superfamily.
Proteins (2021) 89(6) : 632-638

Shrestha R, Fajardo JE, Fiser A
Residue-based pharmacophore approaches to study protein-protein interactions.
Curr Opin Struct Biol (2021) 67, 205-211

Zepecki JP, Karambizi D, Fajardo JE, Snyder KM, Guetta-Terrier C, Tang OY, Chen JS, Sarkar A, Fiser A, Toms SA, Tapinos N
miRNA-mediated loss of m6A increases nascent translation in glioblastoma.
PLoS Genet (2021) 17(3) : e1009086

Shrestha R, Garrett-Thomson SC, Liu W, Almo SC, Fiser A
Redesigning HVEM Interface for Selective Binding to LIGHT, BTLA, and CD160.
Structure (2020) 28(11) : 1197-1205.e2

Zhan HQ, Najmi M, Lin K, Aluri S, Fiser A, Goldman ID, Zhao R
A proton-coupled folate transporter mutation causing hereditary folate malabsorption locks the protein in an inward-open conformation.
J Biol Chem (2020) 295(46) : 15650-15661

Fajardo JE, Fiser A
A Designer Quest for the Achilles' Heel of Influenza.
Structure (2020) 28(10) : 1083-1084

Fajardo JE, Shrestha R, Gil N, Belsom A, Crivelli SN, Czaplewski C, Fidelis K, Grudinin S, Karasikov M, Karczynska AS, Kryshtafovych A, Leitner A, Liwo A, Lubecka EA, Monastyrskyy B, Pages G, Rappsilber J, Sieradzan AK, Sikorska C, Trabjerg E, Fiser A
Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.
Proteins (2020) 88(3) : 540

Gil N, Fajardo EJ, Fiser A
Discovery of receptor-ligand interfaces in the immunoglobulin superfamily.
Proteins (2020) 88(1) : 135-142

Shrestha R, Garrett SC, Almo SC, Fiser A
Computational Redesign of PD-1 Interface for PD-L1 Ligand Selectivity.
Structure (2019) 27(5) : 829-836.e3

Viswanathan R, Fajardo E, Steinberg G, Haller M, Fiser A
Protein-protein binding supersites.
PLoS Comput Biol (2019) 15(1) : e1006704

Shrestha R, Fajardo E, Gil N, Fidelis K, Kryshtafovych A, Monastyrskyy B, Fiser A
Assessing the accuracy of contact predictions in CASP13.
Proteins (2019) 87(12) : 1058-1068

Gil N, Fiser A
The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.
Bioinformatics (2018) ,06:27

Gil N, Fiser A
Identifying Functionally Informative Evolutionary Sequence Profiles.
Bioinformatics (2018) 34:8, 1278-1286

Yap EH, Fiser A
ProtLID, a Residue-Based Pharmacophore Approach to Identify Cognate Protein Ligands in the Immunoglobulin Superfamily.
Structure (2016) 24(12) : 2217-2226

Dybas JM, Fiser A
Development of a motif-based topology-independent structure comparison method to identify evolutionarily related folds.
Proteins (2016) 84(12) : 1859-1874

Vallat B, Madrid-Aliste C, Fiser A
Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.
PLoS Comput Biol (2015) 11(8) : e1004419

Pujato M, Kieken F, Skiles AA, Tapinos N, Fiser A.
Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes.
Nucleic Acids Res (2014);4222:13500-12

Khafizov K, Madrid-Aliste C, Almo SC, Fiser A
Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative.
Proc Natl Acad Sci U S A (2014) 111(10) : 3733-8

Yap EH, Rosche T, Almo S, Fiser A
Functional clustering of immunoglobulin superfamily proteins with protein-protein interaction information calibrated hidden markov model sequence profiles.
J Mol Biol (2014) 426(4) : 945-61

Rubinstein R, Ramagopal UA, Nathenson SG,Almo SC, Fiser A
Functional classification of immune regulatory proteins.
Structure (2013) 21(5): 766-76

Menon V, Vallat BK, Dybas JM, Fiser A
Modeling proteins using a super-secondary structure library and NMR chemcial shift information.
Structure (2013) 21(6): 891-9

Pujato M, MacCarthy T, Fiser A, Bergman A.
The underlying molecular and network level mechanism in the evolution of robustness in gene regulatory networks.
Plos Comput. Biol. (2013) 9(1)