RESUMO
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has become the dominant infective strain. We report the structures of the Omicron spike trimer on its own and in complex with angiotensin-converting enzyme 2 (ACE2) or an anti-Omicron antibody. Most Omicron mutations are located on the surface of the spike protein and change binding epitopes to many current antibodies. In the ACE2-binding site, compensating mutations strengthen receptor binding domain (RBD) binding to ACE2. Both the RBD and the apo form of the Omicron spike trimer are thermodynamically unstable. An unusual RBD-RBD interaction in the ACE2-spike complex supports the open conformation and further reinforces ACE2 binding to the spike trimer. A broad-spectrum therapeutic antibody, JMB2002, which has completed a phase 1 clinical trial, maintains neutralizing activity against Omicron. JMB2002 binds to RBD differently from other characterized antibodies and inhibits ACE2 binding.
Assuntos
Enzima de Conversão de Angiotensina 2/química , Anticorpos Neutralizantes/química , Anticorpos Antivirais/química , SARS-CoV-2/química , SARS-CoV-2/imunologia , Glicoproteína da Espícula de Coronavírus/química , Enzima de Conversão de Angiotensina 2/metabolismo , Anticorpos Neutralizantes/imunologia , Anticorpos Neutralizantes/metabolismo , Anticorpos Neutralizantes/uso terapêutico , Anticorpos Antivirais/imunologia , Anticorpos Antivirais/metabolismo , Sítios de Ligação , Microscopia Crioeletrônica , Epitopos , Humanos , Fragmentos Fab das Imunoglobulinas/química , Fragmentos Fab das Imunoglobulinas/imunologia , Fragmentos Fab das Imunoglobulinas/metabolismo , Modelos Moleculares , Mutação , Ligação Proteica , Conformação Proteica , Domínios Proteicos , Domínios e Motivos de Interação entre Proteínas , Multimerização Proteica , Subunidades Proteicas/química , SARS-CoV-2/genética , SARS-CoV-2/metabolismo , Glicoproteína da Espícula de Coronavírus/genética , Glicoproteína da Espícula de Coronavírus/imunologia , Glicoproteína da Espícula de Coronavírus/metabolismo , TermodinâmicaRESUMO
AIM: To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence. METHODS: With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets. RESULTS: Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that â¼30% of human proteins were potential drug targets, and â¼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access. CONCLUSION: Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.
