Biology subjects

Donald, B. R.

Publications and source records attributed to Donald, B. R..

11 recordsLinked to original sources

bioRxiv2025

CCK* (Convex Closure K*): A Suite of Algorithms for the De Novo Design of L- and D-peptide Binders

The computational design of L-peptides and their mirror-image counterparts, D-peptides, is an active area in drug design. Peptide therapeutics offer exceptional structural diversity and high binding specificity, while D-peptides additionally confer critical advantages such as proteolytic resistance. Progress in de novo D-peptide design has been hindered by the absence of evolutionary context and limited structural data, both of which underpin the deep learning methods widely used in L-peptide design. Consequently, a robust framework capable of designing both L- and D-peptides should integrate data-driven inference with first-principles, physics-based modeling. Here, we introduce a unified computational framework that supports de novo design of both L- and D-peptides, thereby expanding the accessible design space across both chiral spaces. Convex Closure K* (CCK*) is a suite of chirality-agnostic algorithms: SCOPE, MONTAGE, and ARISE. SCOPE uses geometry as a proxy for chemical energetics, computing convex hull representations of rotameric states to rapidly generate multi-sequence protein contact maps. MONTAGE employs geometric hashing in conjunction with the K* algorithm to generate and rank backbone scaffolds according to their suitability for sequence design. ARISE is a K*-based sequence design algorithm that performs iterative residue assignment in an undirected graph to design high-affinity peptide sequences. We apply the full CCK* suite to six de novo design tasks, benchmarking chirality-preserving and chirality-inverting designs in both homochiral and heterochiral complexes.

bioinformatics↗

bioRxiv2025

Predicting pose distribution of protein domains connected by flexible linkers is an unsolved problem

In CASP16, we assessed the ability of computational methods to predict the distribution of relative orientations of two domains tethered by a flexible linker. The range of interdomain distances and orientations (poses) of such domain-linker-domain (D-L-D) proteins can play an important role in protein function, allostery, aggregation, and the thermodynamics of binding. The CASP16 Conformational Ensembles Experiment included two challenges to predict the interdomain pose distribution of a Staphylococcal protein A (SpA) D-L-D construct, called ZLBT-C, in which two of SpAs five nearly identical domains are connected by either (1) a six-residue wild-type (WT) linker (kadnkf), or (2) an all-glycine (Gly6) linker. The wild-type linker has a highly conserved sequence and is thought to contribute to the energetic barrier for binding with host antibodies. Ground truth was provided by nuclear magnetic resonance (NMR) residual dipolar coupling (RDC) data on WT protein and small angle X-ray scattering (SAXS) data on both proteins in solution. Twenty-five predictor groups submitted 35 sets of predicted conformational distributions, in the form of population-weighted finite ensembles of discrete structures. Unlike traditional CASP assessments that compare predicted atomic models to experimental atomic models, the accuracy of these predictions was assessed by back-calculating NMR RDCs and SAXS curves from each ensemble of atomic models and comparing these results to respective experimental data. Accuracy was also assessed by using kernelization to compare ensembles to the continuous orientational distributions optimally fit to experimental data. In our assessment, predictions spanned a wide range of accuracy, but none were close fits to the combined NMR and SAXS data. In addition, none were able to recapitulate the observed difference between WT and Gly6 proteins, as observed in the SAXS data. These results, and our analysis, highlighted strengths and weaknesses, plus complementarity of NMR RDC and SAXS analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/650885v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@148f520org.highwire.dtl.DTLVardef@d5450eorg.highwire.dtl.DTLVardef@1a8c6aborg.highwire.dtl.DTLVardef@18b2b5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Has AlphaFold 3 Solved the Protein Folding Problem for D-Peptides?

Due to their favorable therapeutic properties, including improved stability, bioavailability, and membrane permeability, D-peptides that bind biological L-proteins represent an important class of systems in computational drug design. A reliable in silico workflow for these systems must correctly preserve stereochemistry while predicting fold and binding pose. The AlphaFold 3 (AF3) model reported by Abramson et al. (2024) enforces a strict chirality violation penalty to maintain chiral centers from model inputs and is reported to have a low chirality violation rate of only 4.4% on a PoseBusters benchmark containing diverse chiral molecules. Herein, we report the results of 3,255 black-box experiments with AF3 to evaluate its ability to predict the fold, chirality, and binding pose of D-peptides in heterochiral complexes. Despite inputs specifying explicit D-stereocenters, we report that the AF3 chirality violation rate for D-peptide binders is much higher at 51% across all evaluated predictions; on average the model is as accurate as chance (random chirality choice, L or D, for each peptide residue). Increasing the number of seeds failed to improve this violation rate. The AF3 predictions exhibit incorrect folds and binding poses, with D-peptides commonly oriented incorrectly in the L-protein binding interface. Confidence metrics returned by AF3 also fail to distinguish predictions with low chirality violation and correct docking vs. predictions with high chirality violation and incorrect docking. We conclude that AF3 is a poor predictor of D-peptide chirality, fold, and binding pose and propose solutions to address these limitations. Significance StatementAlphaFold 3 (AF3) is a model trained to predict protein interactions. This algorithm is tuned to respect chiral centers (L and D). Changing the chirality of even one protein residue can significantly alter chemical properties such as binding and stability. Therefore, an algorithm should exhibit a chiral center error rate of 0%. Although the original AF3 authors reported a 4.4% chirality violation rate, we have found that the rate for D-peptides is much higher at[~] 50%. Our data reveal a crucial structural prediction error in AF3 and demonstrate that this widely used model is as accurate on average as chance (random chirality choice, L or D, for each peptide residue). These results indicate structure prediction of D-peptides is an outstanding problem.

bioinformatics↗

bioRxiv2024

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides--evaluation along a replication/restitution axis--and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets endogenous ligands, validating the peptides potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

bioinformatics↗

bioRxiv2022

DISRUPTOR: Computational identification of oncogenic mutants disrupting protein interactions

We report an Osprey-based computational protocol to prospectively identify oncogenic mutations that act via disruption of molecular interactions. It is applicable to analyze both protein-protein and protein-DNA interfaces and has been validated on a dataset of clinically relevant mutations. In addition, it was used to predict previously uncharacterized patient mutations in CDK6 and p16 genes, which were experimentally confirmed to impair complex formation.

cancer biology↗

bioRxiv2022

Resistor: an algorithm for predicting resistance mutations using Pareto optimization over multistate protein design and mutational signatures

Resistance to pharmacological treatments is a major public health challenge. Here we report RO_SCPLOWESISTORC_SCPLOW--a novel structure- and sequence-based algorithm for drug design providing prospective prediction of resistance mutations. RO_SCPLOWESISTORC_SCPLOW computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity ({Delta}Ka) of the (1) drug and (2) endogenous ligand upon a proteins mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. To validate RO_SCPLOWESISTORC_SCPLOW, we applied it to kinase inhibitors targeting EGFR and BRAF in lung adenocarcinoma and melanoma. RO_SCPLOWESISTORC_SCPLOW correctly identified eight clinically significant EGFR resistance mutations, including the "gatekeeper" T790M mutation to erlotinib and gefitinib and five known resistance mutations to osimertinib. Furthermore, RO_SCPLOWESISTORC_SCPLOW predictions are consistent with sensitivity data on BRAF inhibitors from both retrospective and prospective experiments using the KinCon biosensor technology. RO_SCPLOWESISTORC_SCPLOW is available in the open-source protein design software OSPREY.

bioinformatics↗

bioRxiv2020

Chiral evasion and stereospecific antifolate resistance in Staphylococcus aureus

Antimicrobial resistance is a health care crisis. The resistance-conferring mutation F98Y in Staphylococcus aureus dihydrofolate reductase (SaDHFR) reduces effectiveness of antifolates, e.g., trimethoprim (TMP). Although propargyl-linked antifolates (PLAs) are much more resilient than TMP towards F98Y, this substitution still vitiates their inhibition potency. Surprisingly, differences in the enantiomeric configuration at the stereogenic center of PLAs influence the isomeric state of NADPH cofactor. Is resistance correlated with chiral evasion? A mechanism of action underpinning this coupling is unknown. To understand the molecular basis of F98Y-mediated resistance and how PLAs inhibition drives NADPH isomeric states, we used OSPREY to analyze a comprehensive suite of structural, biophysical, biochemical, and computational data. We present a model showing how F98Y SaDHFR exploits a different anomeric configuration of NADPH to evade certain PLAs inhibition, while other PLAs remain resilient to resistance. Our model should enable general design of inhibitors that are resilient to chiral evasion.

biochemistry↗

bioRxiv2019

Computational Analysis of Energy Landscapes Reveals Dynamic Features that Contribute to Binding of Inhibitors to CFTR-Associated Ligand

PDZ domains are small protein-binding domains that interact with short, mostly C-terminal peptides and play important roles in cellular signaling and the trafficking and localization of ion channels. The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibro-sis transmembrane conductance regulator (CFTR) and mediates degradation of mature CFTR through lysosomal pathways. Inhibition of the CALP:CFTR interaction has been explored as a potential therapeutic avenue for cystic fibrosis (CF).1 Previously, we reported2 the ensemble-based computational design of a novel 6-residue peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor of CALP to date. This inhibitor, kCAL01, was designed using OSPREY3 and displayed significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP (PDB ID: 6OV7). To elucidate the structural basis for the enhanced binding efficiency of kCAL01, we compare this structure to that of a previously developed inhibitor of CALP, iCAL36 (PDB ID: 4E34). In addition to per-forming traditional structural analysis, we compute the side-chain energy landscapes for each structure using the recently developed MARK* partition function approximation algorithm.4 Analysis of these energy landscapes not only enables approximation of binding thermodynamics for these structural models of CALP:inhibitor binding, but also foregrounds important structural features and reveals dynamic features, both of which contribute to the comparatively efficient binding of kCAL01. The investigation of energy landscapes complements traditional analysis of the few low-energy conformations found in crystal structures, and provides information about the entire conformational ensemble that is accessible to a protein structure model. Finally, we compare the previously reported NMR-based design model ensemble for kCAL01 vs. the new crystal structure and show that, despite the notable differences between the CALP NMR model and crystal structure, many significant features are successfully captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design algorithm eschewing ensembles would likely miss the kCAL01 sequence entirely.\n\nGraphical TOC Entry\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC=\"FIGDIR/small/720342v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@2a1036org.highwire.dtl.DTLVardef@18e5fe0org.highwire.dtl.DTLVardef@38ab86org.highwire.dtl.DTLVardef@84f1b5_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2019

Toward Broad Spectrum DHFR inhibitors Targeting Trimethoprim Resistant Enzymes Identified in Clinical Isolates of Methicillin-Resistant Staphylococcus aureus

The spread of plasmid borne resistance enzymes in clinical Staphylococcus aureus isolates is rendering trimethoprim and iclaprim, both inhibitors of dihydrofolate reductase (DHFR), ineffective. Continued exploitation of these targets will require compounds that can broadly inhibit these resistance-confering isoforms. Using a structure-based approach, we have developed a novel class of ionized non-classical antifolates (INCAs) that capture the molecular interactions that have been exclusive to classical antifolates. These modifications allow for a greatly expanded spectrum of activity across these pathogenic DHFR isoforms, while maintaining the ability to penetrate the bacterial cell wall. Using biochemical, structural and computational methods, we are able to optimize these inhibitors to the conserved active sites of the endogenous and trimethoprim resistant DHFR enzymes. Here, we report a series of INCA compounds that exhibit low nanomolar enzymatic activity and potent cellular activity with human selectivity against a panel of clinically relevant TMPR MRSA isolates.

biochemistry↗

bioRxiv2018

OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

We present O_SCPCAPOSPREYC_SCPCAP 3.0, a new and greatly improved release of the O_SCPLOWOSPREYC_SCPLOW protein design software. O_SCPLOWOSPREYC_SCPLOW 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of O_SCPLOWOSPREYC_SCPLOW when running the same algorithms on the same hardware. Moreover, O_SCPLOWOSPREYC_SCPLOW 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of O_SCPLOWOSPREYC_SCPLOWO_SCPCAP, C_SCPCAPO_SCPLOWOSPREYC_SCPLOW 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that O_SCPLOWOSPREYC_SCPLOW 3.0 accurately predicts the effect of mutations on protein-protein binding. O_SCPLOWOSPREYC_SCPLOW 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.\n\nO_TEXTBOX\n\nWe present the third major release of the OSPREY protein design software, along with comparisons to experimental data that confirm its ability to optimize protein mutants for desired functions. osprey 3.0 has significant efficiency, ease-of-use, and algorithmic improvements over previous versions, including GPU acceleration and a new Python interface.\n\nC_TEXTBOX\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC=\"FIGDIR/small/306324v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@f08b0dorg.highwire.dtl.DTLVardef@393b61org.highwire.dtl.DTLVardef@16997ddorg.highwire.dtl.DTLVardef@1713405_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2017

Continuous interdomain orientation distributions reveal components of binding thermodynamics

1The flexibility of biological macromolecules is an important structural determinant of function. Unfortunately, the correlations between different motional modes are poorly captured by discrete ensemble representations. Here, we present new ways to both represent and visualize correlated interdomain motions. Interdomain motions are determined directly from residual dipolar couplings (RDCs), represented as a continuous conformational distribution, and visualized using the disk-on-sphere (DoS) representation. Using the DoS representation, features of interdomain motions, including correlations, are intuitively visualized. The representation works especially well for multidomain systems with broad conformational distributions. This analysis also can be extended to multiple probability density modes, using a Bingham mixture model. We use this new paradigm to study the interdomain motions of staphylococcal protein A, which is a key virulence factor contributing to the pathogenicity of S. aureus. We capture the smooth transitions between important states and demonstrate the utility of continuous distribution functions for computing components of binding thermodynamics. Such insights allow the dissection the dynamic structural components of functionally important intermolecular interactions.

biophysics↗