Evidence map›Paper›PMID 40376405›Full record

ReviewBiophysical reviews2025

Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights.

Fernando Luís Barroso da Silva, Karen Paco, Aatto Laaksonen, Animesh Ray

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fernando Luís Barroso da SilvaDepartamento de Ciências Biomoleculares, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Av Prof Zeferino Vaz, S/no, Ribeirão Preto, São Paulo BR-14040-903 Brazil.
Karen PacoRiggs School of Applied Life Sciences, Keck Graduate Institute, 535 Watson Dr., Claremont, CA 91711 USA.
Aatto LaaksonenDepartment of Chemistry, Arrhenius Laboratory, Stockholm University, Svante Arrhenius Väg 8, 106 91 Stockholm, Sweden.
Animesh RayRiggs School of Applied Life Sciences, Keck Graduate Institute, 535 Watson Dr., Claremont, CA 91711 USA.

Funding

Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigenR01AI169543 · NIAID · KECK GRADUATE INST OF APPLIED LIFE SCIS · PI HERNANDEZ, JENIFFER BERTHA, LONARDI, STEFANO · 2021 to 2023
$3.1M
NIAID NIH HHS R01 AI169543
6 · The paper itself

Abstract

The spike protein encoded by the SARS-CoV-2 has become one of the most studied macromolecules in recent years due to its central role in COVID-19 pathogenesis. The spike protein's receptor-binding domain (RBD) directly interacts with the host-encoded receptor protein, ACE2. This review critically examines computational insights into RBD's interaction with ACE2 and with therapeutic antibodies designed to interfere with this interaction. We begin by summarizing insights from early computational studies on pre-pandemic SARS-CoV-1 RBD interactions and how these early studies shaped the understanding of SARS-CoV-2. Next, we highlight key theoretical contributions that revealed the molecular mechanisms behind the binding affinity of SARS-CoV-2 RBD against ACE2, and the structural changes that have enhanced the infectivity of emerging variants. Special attention is given to the "RBD charge rule", a predictive framework for determining variant infectivity based on the electrostatic properties of the RBD. Towards applying the computational insights to therapy, we discuss a multiscale computational protocol for optimizing monoclonal antibodies to improve binding affinity across multiple spike protein variants, including representatives from the Omicron family. Finally, we explore how these insights can inform the development of future vaccines and therapeutic interventions for combating future coronavirus diseases.

Indexed as

AntibodiesComplexationElectrostatic interactionsMolecular simulationpH effectsTransmissibilityVirus

Identifiers

PMID40376405
PMCPMC12075047

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.