Computational Mapping of Conformational Dynamics and Interaction Hotspots of Human VISTA with pH-Selective Antibodies
Bibliographic record
Abstract
The V-domain Ig suppressor of T-cell activation (VISTA) is a critical negative immune checkpoint protein that regulates T-cell-mediated anticancer immune responses, making it a promising target for immunotherapy. Unlike other checkpoint proteins, VISTA activity is moderated by pH and engages with distinct ligands under variable pH conditions to promote immune suppression. Understanding the structural dynamics of VISTA and developing pH-selective antibodies to disrupt its interactions remain significant areas of research. Recently, two X-ray crystal structures of VISTA bound to pH-selective monoclonal antibodies have been reported. In this study, we probed the structural stability, conformational dynamics, and molecular interactions of VISTA in its apo state and when bound to these antibodies. A combination of atomistic modeling, molecular dynamics simulations, binding free energy calculations, energy decomposition analyses, and computational alanine scanning was employed. The results revealed the critical roles of key arginine residues (R90 and R74) that shield the hydrophobic core of VISTA, maintaining its structural integrity. Distinct VISTA regions, including the CC' loop, C'C″ segments, and FG loop, were found to play pivotal roles in antibody binding. Electrostatic interactions involving R86, R159, and E157, alongside an extensive π-π stacking network facilitated by Y69, Y73, and F94, were identified as key contributors to the complex stability and binding affinity. Overall, this study provides detailed insights into the structural dynamics and molecular interactions of VISTA with pH-selective antibodies. These findings enhance our understanding of VISTA's molecular mechanisms and lay a foundation for the rational design of improved therapeutics targeting immune checkpoint proteins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".