Biology subjects

Gomes, P. S. F. C.

Publications and source records attributed to Gomes, P. S. F. C..

3 recordsLinked to original sources

bioRxiv2023

Effects of Variants of Concern Mutations on the Force-Stability of the SARS-CoV-2:ACE2 Interface and Virus Transmissibility

Viruses mutate under a variety of selection pressures, allowing them to continuously adapt to their hosts. Mutations in SARS-CoV-2 have shown effective evasion of population immunity and increased affinity to host factors, in particular to the cellular receptor ACE2. However, in the dynamic environment of the respiratory tract forces act on the binding partners, which raises the question whether not only affinity, but also force-stability of the SARS-CoV-2:ACE2 bond, might be a selection factor for mutations. Here, we use magnetic tweezers (MT) to study the effect of amino acid substitutions in variants of concern (VOCs) on RBD:ACE2 bond kinetics with and without external load. We find higher affinity for all VOCs compared to wt, in good agreement with previous affinity measurements in bulk. In contrast, Alpha is the only VOC that shows significantly higher force stability compared to wt. Investigating the RBD:ACE2 interactions with molecular dynamics simulations, we are able to rationalize the mechanistic molecular origins of this increase in force-stability. Our study emphasizes the diversity of contributions to the assertiveness of variants and establishes force-stability as one of several factors for fitness. Understanding fitness-advantages opens the possibility for prediction of likely mutations allowing rapid adjustment of therapeutics, vaccination, and intervention measures.

biophysics↗

bioRxiv2022

Bridging the gab between in vitro and in silico single-molecule force spectroscopy

Staphylococci bacteria use an arsenal of virulence factors, mainly composed of proteins such as adhesins, to target and adhere to their host. Adhesins play critical roles during infection, mainly during the early steps of adhesion when cells are exposed to high mechanical stress. S. epidermidis SdrG:Fg{beta} force resilience has been investigated using AFM-based single molecule force spectroscopy experiments paired with steered molecular dynamics (SMD) simulations. However, there is still a gap between both kinds of experiments at high force-loading rates. Here, we leveraged the high-speed of coarse-grained (CG) SMD simulations to bridge the gap between the data obtained in vitro and in silico with all-atom SMD. We used the DHS theory to connect the two types of SMD simulations and the predictions are consistent with theory and experimentation. We believe that, when associated with all-atom SMD, course-grained SMD can be a powerful ally to help explain and complement the results of single-molecule force spectroscopy experiments.

biophysics↗

bioRxiv2022

Protein structure prediction in the era of AI: challenges and limitations when applying to in-silico force spectroscopy

Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how several mechanoactive proteins respond to mechanical forces. However, even SMFS has its limitations, including the lack of detailed structural information during force-loading experiments. That is where molecular dynamics (MD) methods shine, bringing atomistic details with femtosecond time-resolution. However, MD heavily relies on the availability of high-resolution structures, which is not available for most proteins. For instance, the Protein Data Bank currently has 192K structures deposited, against 231M protein sequences available on Uniprot. But many are betting that this gap might become much smaller soon. Over the past year, the AI-based AlphaFold created a buzz on the structural biology field by being able to, for the first time, predict near-native protein folds from their sequences. For some, AlphaFold is causing the merge of structural biology with bioinformatics. In this perspective, using an in silico SMFS approach, we investigate how reliable AlphaFold structure predictions are to investigate mechanical properties of staph bacteria adhesins proteins. Our results show that AlphaFold produce extremally reliable protein folds, but in many cases is unable to predict high-resolution protein complexes accurately. Nonetheless, the results show that AlphaFold can revolutionize the investigation of these proteins, particularly by allowing high-throughput scanning of protein structures. Meanwhile, we show that the AlphaFold results need to be validated and should not be employed blindly, with the risk of obtaining an erroneous protein mechanism.

bioinformatics↗