Biology subjects

Gomes, D. E. B.

Publications and source records attributed to Gomes, D. E. B..

3 recordsLinked to original sources

bioRxiv2022

May the force be with you: the role of hyper-mechanostability of the bone sialoprotein binding protein during early stages of Staphylococci infections

The bone sialoprotein-binding protein (Bbp) is a mechanoactive MSCRAMM protein expressed on the surface of Staphylococcus aureus that mediates adherence of the bacterium to fibrinogen- (Fg), a component of the bone and dentine extracellular matrix of the host cell. Mechanoactive proteins like Bbp have key roles in several physiological and pathological processes. Particularly, the Bbp:Fg interaction is important in the formation of biofilms, an important virulence factor of pathogenic bacteria. Here, we investigated the mechanostability of the Bbp:Fg complex using in silico single-molecule force spectroscopy (SMFS), in an approach that combines results from all-atom and coarse-grained steered molecular dynamics (SMD) simulations. Our results show that Bbp is the most mechanostable MSCRAMM investigated thus far, reaching rupture forces beyond the 2 nN range in typical experimental SMFS pulling rates. Our results show that high force-loads, which are common during initial stages of bacterial infection, stabilize the interconnection between the proteins amino acids, making the protein more "rigid". Our results offer new insights that are crucial on the development of novel anti-adhesion strategies.

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↗