Performance of ZDOCK and IRAD in CAPRI rounds 39-45

AI-augmented physics-based docking for antibody-antigen complex prediction

MOTIVATION

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens.

METHODS

We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In this study, we also compare docking performance with AlphaFold and Boltz-1, the new state-of-the-art. We distinguish between two types of success tailored to specific downstream applications: (i) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and (ii) criteria for producing higher-quality models suitable for engineering purposes.

RESULTS

We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental, and that prioritization of models is essential for achieving good performance. In a scenario analogous to docking using a crystallized antigen, our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking also shows improvements over Boltz-1, but those are less pronounced. Docking performance is still noticeably lower than AlphaFold3 in both epitope mapping and antibody design use cases. We observe a strong dependence on CDR-H3 loop length for physics-based tools on their ability to successfully predict. This helps define an applicability range where physics-based docking can be competitive to the newer generation of AI tools.

AVAILABILITY AND IMPLEMENTATION

The AF2 rescoring scripts are available at github.com/gaudreaultfnrc/AF2-Rescoring.

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.

Targeted drug delivery to the thrombus by fusing streptokinase with a fibrin-binding peptide (CREKA): an in silico study

Aim: Streptokinase has poor selectivity and provokes the immune response. In this study, we used in silico studies to design a fusion protein to achieve targeted delivery to the thrombus. Materials & methods: Streptokinase was analyzed computationally for mapping. The fusion protein modeling and quality assessment were carried out on several servers. The enzymatic activity and the stability of the fusion protein and its complex with plasminogen were assessed through molecular docking analysis and molecular dynamics simulation respectively. Results: Physicochemical properties analysis, protein quality assessments, protein-protein docking and molecular dynamics simulations predicted that the designed fusion protein is functionally active. Conclusion: Our results showed that this fusion protein might be a prospective candidate as a novel thrombolytic agent with better selectivity.

Discovery of Kinetin in inhibiting colorectal cancer progression via enhancing PSMB1-mediated RAB34 degradation

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Understanding the underlying mechanism driving CRC progression and identifying potential therapeutic drug targets are of utmost urgency. We previously utilized LC-MS-based proteomic profiling to identify proteins associated with postoperative progression in stage II/III CRC. Here, we revealed that proteasome subunit beta type-1 (PSMB1) is an independent predictor for postoperative progression in stage II/III CRC. Mechanistically, PSMB1 binds directly to onco-protein RAB34 and promotes its proteasome-dependent degradation, potentially leading to the inactivation of the MEK/ERK signaling pathway and inhibition of CRC progression. To further identify potential anticancer drugs, we screened a library of 2509 FDA-approved drugs using computer-aided drug design (CADD) and identified Kinetin as a potentiating agent for PSMB1. Functional assays confirmed that Kinetin enhanced the interaction between PSMB1 and RAB34, hence facilitated the degradation of RAB34 protein and decreased the MEK/ERK phosphorylation. Kinetin suppresses CRC progression in patient-derived xenograft (PDX) and liver metastasis models. Conclusively, our study identifies PSMB1 as a potential biomarker and therapeutic target for CRC, and Kinetin as an anticancer drug by enhancing proteasome-dependent onco-protein degradation.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2

Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC₅₀ ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC₅₀ values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.

Molecular dynamics study of CDC25BR492L mutant causing the activity decrease of CDC25B

Cell division cycle 25B (CDC25B) was responsible for regulating the various stages of cell division in the cell cycle. R492L was one of the common types of CDC25B mutants. Researches showed that compared to CDC25B^WT, CDC25B^R492L mutant had a ∼100-fold reduction in the rate constant for forming phosphatase intermediate (k₂). However, the molecular basis of how the CDC25B^R492L mutant influenced the process of binding between CDC25B and CDK2/CyclinA was not yet known. Therefore, the optimizations of three-dimensional structure of the CDC25B^WT-CDK2/CyclinA system and the CDC25B^R492L-CDK2/CyclinA system were constructed by ZDOCK and RDOCK, and five methods were employed to verify the reasonability of the docking structure. Then the molecular dynamics simulations on the two systems were performed to explore the reason why CDC25B^R492L mutant caused the weak interactions between CDC25B^R492L and CDK2/CyclinA, respectively. The remote docking site (Arg488-Tyr497) and the second active site (Lys538-Arg544) of CDC25B were observed to have high fluctuations in the CDC25B^R492L-CDK2/CyclinA system with post-analysis, where the high fluctuation of these two regions resulted in weak interactions between CD25B and CDK2. In addition, Asp38-Glu42 and Asp206-Asp210 of CDK2 showed the slightly descending fluctuation, and CDK2 revealed an enhanced the self-interaction, which made CDK2 keep a relatively stable state in the CDC25B^R492L-CDK2/CyclinA system. Finally, Leu492 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25B^R492L and CDK2 in the CDC25B^R492L-CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately offered a helpful understanding of the weak interactions between CDC25B^R492L and CDK2.