Biology subjects

Joseph-McCarthy, D.

Publications and source records attributed to Joseph-McCarthy, D..

7 recordsLinked to original sources

bioRxiv2026

The Influence of Ligands on AlphaFold3 Prediction of Cryptic Pockets

Cryptic pockets are binding sites that are formed or exposed upon a conformational change. They represent an important class of potentially druggable binding sites. Reliably predicting cryptic pockets capable of binding ligands, however, remains a challenge. Herein we examine the use of AlphaFold 3 (AF3) for generating realistic conformational ensembles that include known cryptic pockets. We find that AF3 is generally able to reproduce the scale of conformational change required for cryptic site formation. When given a cryptic-site ligand for the protein, AF3 predominantly predicts conformations competent to bind the ligand in the cryptic site; without the ligand, conformations lacking the cryptic pocket generally dominate. While the results may reflect a bias toward memorized structural priors, the level of detrimental memorization appears to be limited. We also show that the choice of the ligand can significantly impact the predictions, and that AF3 is able to produce models with the ligand correctly positioned. Variability in ligand position, however, suggests that generating ensembles of co-folded predictions is critical to enhancing the likelihood of obtaining a correct binding mode. Overall, AF3-generated protein-ligand structural ensembles have potential utility in cryptic-site drug discovery, and they can reveal ligands likely to bind to those sites.

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bioRxiv2026

Towards Understanding the Drivers of Antibody-Antigen Binding

Antibody therapeutics are capable of binding to target antigens with a high degree of specificity and affinity. Gaining an understanding of how antibody-antigen interactions are governed can provide valuable insights that may assist with rational paratope design and epitope prediction. In this work, we apply the FTMap algorithm to systematically characterize binding hot spots - regions on a protein surface that contribute disproportionately to molecular recognition, to a set of 50 antibody-antigen complexes. From our analysis, we find that interface hot spots are typically concentrated on the paratope (antibody side) of the interface, indicating that paratopes typically function as hot spot rich environments in which the antigen can bind. Additionally, we observe that hot spot formation on both sides of the interface is particularly enriched by Trp and Tyr residues, underscoring the key role of aromatic side chains with some amphiphilic character in antibody design. Furthermore, we find that when strong interface hot spots are detected, they tend to persist in the apo conformation, suggesting that there is an inherent structural stability that surrounds core interface hot spots. These findings demonstrate the utility of computational solvent mapping for analyzing protein-protein interfaces, and highlights that at least in most cases antibodies drive antibody-antigen interactions. Statement of SignificanceAntibodies represent an important and expanding class of therapeutics for a range of diseases including infectious diseases, cancers, and autoimmune disorders. Enhancing our fundamental understanding of what drives antibody-antigen interactions is critical to our enhancing our ability to modulate these interactions. This work presents a systematic, physics-based study of antibody-antigen interfaces, and identifies key drivers of binding.

bioinformatics↗

bioRxiv2026

ASSESSMENT OF ALPHAFOLD PROTEIN MODELS FOR SMALL-MOLECULE LIGAND DOCKING

Molecular docking is a powerful computational tool for predicting protein-ligand interactions, widely employed in drug discovery. However, its effectiveness is often constrained by the availability of experimentally resolved X-ray protein structures, a process that is both time consuming and resource-intensive. AlphaFold (AF), a deep learning method, offers an efficient alternative by predicting high-accuracy 3D protein structures directly from amino acid sequences. This study assesses the utility of AF-generated protein models for fragment and larger ligand docking with Glide, a widely used docking approach. The docking workflow is evaluated in an unbiased manner by carrying out binding site identification with FTMap, a binding hot spot prediction software. We show that fragment docking to AF models outperforms docking to the respective unbound protein crystal structures, and performs comparably to docking to the corresponding ligand bound structures when using an unbiased approach. Leveraging computational efficiency of AF model generation, we also employ ensembles of AF models to incorporate protein flexibility. Results show that docking to AF ensembles improves larger-ligand docking compared to docking to singular AF models and outperforms docking to unbound structures. The results provide insights into the effectiveness of integrating AF protein models into docking procedures, highlighting the potential for streamlining computational drug discovery processes. STATEMENT OF SIGNIFICANCEThis work addresses a critical bottleneck in computational drug discovery by demonstrating that AlphaFold (AF) models can serve as an alternative or complement to experimental structures for molecular docking. Specifically, a systematic study assessing Glide docking to rigid AF protein models and ensembles of models compared to experimentally determined ligand-bound and unbound protein X-ray structures was performed. The evaluation employs an unbiased methodology using FTMap-identified binding sites, eliminating the need for prior knowledge of the native ligand binding location. Additionally, protein flexibility is incorporated through a multiseed ensemble approach that generates a conformational ensembles of AF models at minimal computational cost, improving the docking accuracy without the need for ligand-bound templates.

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bioRxiv2025

CDR-aware masked language models for pairedantibodies enable state-of-the-art bindingprediction

BackgroundTherapeutic antibodies are a leading class of biologics, yet their unique architecture poses challenges for computational modeling. Each antibody comprises paired heavy and light variable domains with conserved framework regions that maintain structure and hypervariable complementarity-determining regions (CDRs) that directly contact antigens. This functional asymmetry, where CDRs determine binding specificity while frameworks provide scaffolding, suggests that region-aware training strategies could yield superior representations. Existing protein language models treat all regions uniformly, potentially missing critical features present in CDRs. MethodsWe developed a region-aware pretraining strategy for paired variable domain sequences using two protein language models: a 3 billion parameter model (ESM2) and a compact 600 million parameter model (ESM C). We compared three masking approaches: uniform whole-chain masking, CDR-focused masking, and a hybrid strategy. Final models were trained on over 1.6 million paired antibody sequences and evaluated on binding affinity datasets with over 90,000 antibody variants across six antigens, including single-mutant panels and combinatorial libraries. ResultsHere we show that CDR-focused training produces embeddings with superior predictive performance for antibody-antigen binding. Our approach achieves up to 27% improvements in binding affinity prediction compared to benchmarked antibody models. Remarkably, training exclusively on paired sequences proves sufficient; pretraining on billions of unpaired sequences provides no measurable benefit. Our compact model matches or exceeds larger antibody-specific baselines. ConclusionsThese findings establish that prioritizing paired sequences with CDR-aware supervision over scale and complex training schemes achieves both computational efficiency and predictive accuracy, providing a practical framework for next generation antibody language models.

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bioRxiv2023

COMPUTATIONAL IDENTIFICATION OF ANTIBODY-BINDING EPITOPES FROM MIMOTOPE DATASETS

A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Herein we describe two novel methods for mapping mimotopes to epitopes. The first is an ensemble approach, which combines the prediction results from two existing methods. The second is a novel algorithm named MimoTree, that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes.

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bioRxiv2023

Identification of a Druggable Site on GRP78 at the GRP78-SARS-CoV-2 Interface and Compounds to Disrupt that Interface

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-spike protein-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds have previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

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bioRxiv2022

Machine Learning for the Identification of Viral Attachment Machinery from Respiratory Virus Sequences

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For five families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the five families above provides sufficient information to identify the protein(s) responsible for viral attachment and to permit an assignment of viral family. Random forest models that take as input a set of respiratory viral sequences can classify the protein as "spike" vs. non-spike based on predicted secondary structure elements alone (with 97.8 % correctly classified) or in combination with N-glycosylation related features (with 98.1 % correctly classified). In addition, a Random Forest model developed using the same dataset and only secondary structural elements was able to predict the respiratory virus family of each protein sequence correctly 89.0 % of the time. Models were validated through 10-fold cross-validation as well as bootstrapping. Surprisingly, we showed that secondary structural element and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics.

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