Biology subjects

Dunbrack, R.

Publications and source records attributed to Dunbrack, R..

10 recordsLinked to original sources

bioRxiv2026

Res ipSAE adhuc loquuntur: What the directionality of predicted aligned error in cofolding models can tell us about scoring diverse molecular complexes

AlphaFold 3 (AF3) and Boltz-2 estimate confidence in predicted biomolecular complexes using the interface predicted template modeling (ipTM) score, which is derived from probability distributions of predicted aligned errors (PAEs) between molecular tokens. These errors are directional: the predicted position of tokens in one molecule is evaluated relative to coordinate frames defined by tokens in another. The PAE matrix is therefore asymmetric, but AF3 reports the maximum of the two directional pairwise ipTM scores, whereas Boltz-2 reports them separately. In both programs, the TM-score scaling parameter, d0, depends on molecular system size rather than solely on the component being scored. In protein-protein complexes, including disordered regions and domains unrelated to the interface can further lower ipTM. I previously introduced the interaction predicted score from aligned errors (ipSAE) to address these limitations for protein-protein complexes. Here, I extend ipSAE to interactions involving proteins, nucleic acids, small-molecule ligands, and atomic ions. The framework makes three explicit choices for each molecular pair: which component supplies the alignment frames, which tokens are scored, and how d0 is defined. For different molecular types, I propose aligning on the more complex molecule and scoring the simpler one. For protein-ligand complexes, this entails aligning on protein residues, scoring all ligand heavy atoms without a PAE cutoff, and using a fixed d0 of 2 [A]. In benchmarks of 102 protein-ligand complexes predicted by AF3 and Boltz-2, this ipSAE formulation yields a substantially wider range of confidence scores than native ipTM. On a held-out set of 82 complexes, ipSAE thresholds of 0.5 for AF3 and 0.8 for Boltz-2 classified ligand placements with balanced accuracies of 0.938 and 0.862, respectively, using a ligand RMSD threshold of 5 [A]. Protein-DNA benchmarks support the same directional scoring scheme, while smaller protein-RNA benchmarks provide a preliminary assessment. These results establish a framework for interpreting predicted structural confidence across different classes of biomolecular interactions and suggest that directional scoring may also be useful for computationally designed protein binders. The new freely available ipSAE code is available from: https://github.com/DunbrackLab/IPSAE2.

bioinformatics↗

bioRxiv2026

Tyrosine phosphorylation and dimerization cooperatively activate NAMPT to enable NAD+ synthesis in cancer

Nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD salvage pathway, is frequently upregulated in cancer, yet mechanisms regulating its catalytic activity remain undefined. We identify NAMPT as a direct substrate of multiple proto-oncogenic tyrosine kinases, including ALK, insulin receptor, IGF1R, and PDGFRA. Phosphoproteomics identified NAMPT Y188 as the major phosphorylation site, including the oncogenic fusion kinase NPM1::ALK. NAMPT interacted with NPM1::ALK in the cytoplasm, nucleus, and mitochondria, while Y188 phosphorylation enhanced catalytic activity, NMN/NAD biosynthesis, and downstream metabolism. Conversely, the Y188F mutant reduced enzymatic activity, proliferation, and clonogenicity, whereas disrupting dimerization similarly impaired phosphorylation and function. Interactome analyses showed phosphorylation and dimerization cooperatively remodel NAMPT-associated networks, enriching phosphorylated dimers for metabolic/redox regulators and monomeric NAMPT for ribosome biogenesis. NAMPT inhibition suppressed the growth of both ALK inhibitor-sensitive and -resistant lymphoma cells and enhanced the efficacy of ALK inhibition, revealing kinase-dependent NAMPT activation as a metabolic vulnerability in oncogene-driven cancers.

cancer biology↗

bioRxiv2026

Structure-guided analysis and prediction of human E2-E3 ligase pairing specificity

Protein ubiquitination, directed by specific E3 ligases, constitutes the primary cellular pathway for selective protein degradation. In addition to targeting proteins for degradation, ubiquitination can mediate new protein-protein interactions, and otherwise modulate protein function, thereby regulating key cellular processes such as DNA repair and immune responses. Recently, Proteolysis-Targeting Chimeras (PROTACs), and related proximity-inducing agents, have revealed the significant therapeutic potential of co-opting ubiquitin ligase activity to induce the selective degradation of disease-relevant proteins. Despite the biological and clinical significance of this pathway, fundamental gaps remain in our understanding of ubiquitination networks, particularly regarding the specificity of E2-E3 interactions and their substrate preferences. In this study, we leverage analysis of experimental structures in the Protein Data Bank (PDB) and use AlphaFold to generate structures of thousands of ubiquitin-E2-E3 ternary complexes. Using these predicted structures and complementary analyses, we develop a machine learning model to predict functional E2-E3 pairings, advancing our ability to map ubiquitination networks and providing structural insights into functional ubiquitin-E2-E3 complexes. We demonstrate the utility of our model by predicting E2 partners for 88 putative E3 ligases lacking any previously known E2 interactors. Notably, we identify a predicted pairing between UBE2C and RNF214, two proteins recently implicated in hepatocellular carcinoma separately but through interrelated pathways, suggesting a potential functional link mediated by RNF214-dependent ubiquitination in partnership with UBE2C. Additionally, we present our web-resource, UbiqCore, making the E2-E3 pairing predictions and ternary complex structures available to the scientific community (https://dunbrack.fccc.edu/ubiqcore). Significance StatementUbiquitination is an essential protein modification that regulates nearly all aspects of biology and is mediated by dozens of ubiquitin-conjugating enzymes (E2s) and hundreds of ubiquitin ligases (E3s). These enzymes are frequently implicated in cancer and other diseases, and their activities can also be harnessed for targeted protein degradation therapies. Despite the biological and clinical significance of ubiquitination, there remains no systematic understanding of which E2s and E3s function together. Here, we combine bioinformatic analysis of experimentally determined structures and AlphaFold modeling of thousands of ubiquitin-E2-E3 complexes with machine learning to predict functional E2-E3 pairings. We also introduce UbiqCore, a web resource that makes these predictions and structures broadly accessible, providing a foundation for mapping ubiquitination networks and guiding future biological and therapeutic discovery.

bioinformatics↗

bioRxiv2025

Res ipSAE loquunt: What's wrong with AlphaFold's ipTM score and how to fix it

AlphaFolds ipTM metric is used to predict the accuracy of structural predictions of protein-protein interactions (PPIs) and the probability that two proteins interact. Many AF2/AF3 users have experienced the phenomenon that if they trim full-length sequence constructs (e.g. from UniProt) to the interacting domains (or domain+peptide), their ipTM scores change, even though the structure prediction of the interaction is unchanged. The reason this happens is due to the mathematical formulation of ipTM in AF2/AF3, which scores the interactions of whole chains. When one chain is a single ordered domain but the other contains lots of disorder or irrelevant domains, ipTM is higher in the presence of those residues than it is when they are trimmed. If both chains in a PPI complex contain large amounts of disorder or accessory domains that do not form the primary domain-domain or domain/peptide interaction, the ipTM score can be significantly lower in the presence of those regions than when they are trimmed. The score then does not accurately represent the accuracy of the structure prediction nor whether the two proteins actually interact. We have solved this problem by: 1) including only residue pairs in the ipTM metric that have good predicted aligned error (PAE) scores; 2) by adjusting the d0 parameter (a function of the length of the query sequences) in the TM score equation to include only the number of residues with good interchain PAEs to the aligned residue; and 3) using the PAE value itself and not the probability distributions over the aligned error to calculate the pairwise residue-residue pTM values that go into the ipTM calculation. The first two are crucial in calculating high ipTMs for domain-domain and domain-peptide interactions even in the presence of many hundreds of residues in disordered regions and/or accessory domains. The third allows us to require only the common output json files of AF2 and AF3 (including the server output) without having to change the AlphaFold code and without affecting the accuracy. It also works on npz file output from Boltz. We show in a benchmark that the new score, called ipSAE (interaction prediction Score from Aligned Errors), is able to separate true from false complexes more efficiently than AlphaFold2s ipTM score. The resulting program is freely available at https://github.com/dunbracklab/IPSAE.

bioinformatics↗

bioRxiv2022

Co-occurring mutations in the POLE exonuclease and non-exonuclease domains define a unique subset of highly mutagenic tumors.

Somatic POLE mutations in the exonuclease domain (ExoD) are prevalent in colorectal cancer (CRC), endometrial cancer (EC), and others and typically lead to dramatically increased tumor mutation burden (TMB). To understand whether non-ExoD mutations also play a role in mutagenesis, we assessed TMB in 447/14541 POLE-mutated CRCs, ECs, and ovarian cancers (OC) based on classification TMB-High (TMB-H) or TMB-Low (TMB-L). TMB-H tumors were segregated as POLE ExoD driver, POLE ExoD driver plus POLE Variant, and POLE Variant TMB-H. Intriguingly, TMB was highest in tumors bearing POLE ExoD driver plus POLE Variant (p<0.001 in CRC and EC, p<0.05 in OC). Integrated analysis of AlphaFold2-modeled POLE models and quantitative estimate of stability indicated that multiple variants had significant impact on functionality. These data indicate that co-occurring POLE variants categorize a unique subset of POLE-driven tumors defined by ultra-high TMB, which has implications for abundance of tumor neoantigens, therapeutic response, and patient outcomes. SignificanceSomatic POLE ExoD driver mutations cause proofreading deficiency that induces high tumor mutation burden (TMB). This study defines a novel modifier role for non-ExoD mutations in POLE ExoD-driven tumors, associated with ultra-high TMB. These data may inform acquisition of tumor neoantigens, tumor classification, therapeutic response, and patient outcomes.

cancer biology↗

bioRxiv2021

PDBrenum: a webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences

The Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In early 2021, the database has more than 175,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions, including binding partners such as ligands, nucleic acids, or other proteins; mutations, and post-translational modifications, thus enabling extensive comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. For instance, some authors may include N-terminal signal peptides or the N-terminal methionine in the sequence numbering and others may not. In addition to the coordinates, there are many fields that contain information regarding specific residues in the sequence of each protein in the entry. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries or a list of UniProt identifiers (e.g., "P04637" or "P53_HUMAN") and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe. AvailabilitySource code is freely available at https://github.com/Faezov/PDBrenum. The webserver is located at: http://dunbrack3.fccc.edu/PDBrenum. Contactbulat.faezov@fccc.edu or roland.dunbrack@fccc.edu.

bioinformatics↗

bioRxiv2021

Kincore: a web resource for structural classification of protein kinases and their inhibitors

The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into 8 classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.

bioinformatics↗

bioRxiv2020

Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding.

Antibody variable domains contain "complementarity determining regions" (CDRs), the loops that form the antigen binding site. CDRs1-3 are recognized as the canonical CDRs. However, a fourth loop sits adjacent to CDR1 and CDR2 and joins the D and E strands on the antibody v-type fold. This "DE loop" is usually treated as a framework region, even though mutations in the loop affect the conformation of the CDRs and residues in the DE loop occasionally contact antigen. We analyzed the length, structure, and sequence features of all DE loops in the Protein Data Bank, as well as millions of sequences from HIV-1 infected and naive patients. We refer to the DE loop as H4 and L4 in the heavy and light chain respectively. Clustering the backbone conformations of the most common length of L4 (6 residues) reveals four conformations: two {kappa}-only clusters, one {lambda}-only cluster, and one mixed {kappa}/{lambda} cluster. The vast majority of H4 loops are length-8 and exist primarily in one conformation; a secondary conformation represents a small fraction of H4-8 structures. H4 sequence variability exceeds that of the antibody framework in naive human high-throughput sequences, and both L4 and H4 sequence variability from {lambda} and heavy germline sequences exceed that of germline framework regions. Finally, we identified dozens of structures in the PDB with insertions in the DE loop, all related to broadly neutralizing HIV-1 antibodies, as well as antibody sequences from high-throughput sequencing studies of HIV-infected individuals, illuminating a possible role in humoral immunity to HIV-1.

bioinformatics↗

bioRxiv2019

A Structurally Validated Sequence Alignmentof All 497 Typical Human Protein Kinase Domains

Studies on the structures and functions of individual kinases have been used to understand the biological properties of other kinases that do not yet have experimental structures. The key factor in accurate inference by homology is an accurate sequence alignment. We present a parsimonious, structure-based multiple sequence alignment (MSA) of 497 human protein kinase domains excluding atypical kinases, even those with related but somewhat different folds. The alignment is arranged in 17 blocks of conserved regions and unaligned blocks in between that contain insertions of varying lengths present in only a subset of kinases. The aligned blocks contain well-conserved elements of secondary structure and well-known functional motifs, such as the DFG and HRD motifs. From pairwise, all-against-all alignment of 272 human kinase structures, we estimate the accuracy of our MSA to be 97%. The remaining inaccuracy comes from a few structures with shifted elements of secondary structure, and from the boundaries of aligned and unaligned regions, where compromises need to be made to encompass the majority of kinases. A new phylogeny of the protein kinase domains in the human genome based on our alignment indicates that ten kinases previously labeled as \"OTHER\" can be confidently placed into the CAMK group. These kinases comprise the Aurora kinases, Polo kinases, and calcium/calmodulin-dependent kinase kinases.

bioinformatics↗

bioRxiv2019

Principles and characteristics of biological assemblies in experimentally determined protein structures

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.

bioinformatics↗