The kinesin released from the MT can possibly bind again to several binding sites , as shown in Fig.

The probability of rebinding to the jth binding site is calculated using the transition rate k A, j.

The value of k A, j decreases as the distance between the unbound kinesin and the jth binding site increases.

In particular, three CL binding sites were identified on UraA: two in the inner leaflet and a single site in the outer leaflet.

Mutation of basic residues in the binding sites resulted in the loss of CL binding in the simulations.

Analysis of the lifetime of interactions between the different lipid types and the 3 CL binding sites was performed by calculating distances (dij) between each binding site (i) and the head group of each of the 3 different lipid types (7').

Whilst the calculation of the lifetime of interactions is sensitive to the cutoff distance used, the trend that CL molecules form more prolonged interactions with CL binding site 1 is retained for all cutoff distances used.

Similarly, for CL binding site 2 it varies from ~ 200 ns to ~ 450 ns, and for CL binding site 3 varies from ~ 100 ns to ~ 300 ns.

Additionally, removing basic residues from the CL binding sites of UraA facing the outer bilayer leaflet (UraAmut-2-CG), facing the inner bilayer leaflet (UraAmut-3-CG) or a combination of the two (UraAmut-1-CG) allowed us to probe if CL molecules bound cooperatively to the three CL binding sites .

These mutations removed the CL binding site in the outer leaflet of the bilayer.

These mutations removed the CL binding sites in the region of UraA facing the inner leaflet of the bilayer.

Recent studies using MD simulations have identified cardiolipin (CL) binding sites in the cytochrome bc1 transporter [8] and in the cytochrome c oxidase [9], and PIPZ binding sites in Kir channels [10,11].

Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands.

Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compounds binding mode in the binding site of the target protein.

nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization.

Other methods use local structural comparisons of small molecule binding sites to infer the localization and specificity of binding pockets [25,26] as well as to infer new ligand interactions in known binding pockets [27].

Thorough the binding site of FLP to ovine COX-1 (IQEH), nAnnoLyze predicts its binding site of the COX-1 human 3D model.

Those ligands are predicted to bind the same predicted binding site of the human COX-1 thanks to its similarity to the crystal structure of optimal cutoff (max value)

Remarkably, the human COX-1 predicted binding site includes the tyrosine 385, which is known to be responsible of the catalytic reaction with the NSAID drugs (Fig.

Here, we examine the structural environment of the PC190723 binding pocket using Pock-etFEATURE, a statistical method that scores the similarity between pairs of small-molecule binding sites based on 3D structure information about the local microenvironment, and molecular dynamics (MD) simulations.

We observed that species and nucleotide-binding state have significant impacts on the structural properties of the binding site , with substantially disparate microenvironments for bacterial species not from the Staphylococcus genus.

Here, we evaluate the PC190723 binding site with currently available crystallographic structures of FtsZ using the structural comparison algorithm PocketFEATURE and all-atom molecular dynamics simulations.

One limitation of the PocketFEATURE algorithm is that evaluation requires some a priori knowledge or estimate of a potential binding site , either from a similar protein structure or through cavity detection software.

PocketFEATURE binding site similarity score is the sum of the best-scoring, non-redundant microenVironment pairs.

Notably, binding site microenVironment comparison performed by PocketFEATURE captures details that are not detected solely through structural analysis.

We sought to determine if FtsZ mutations distort the microenvironment of the PC190723 binding site .

Since the resolution of the averaged tomogram, obtained via alignment and averaging of individual tomograms, was not sufficient to unambiguously identify the location of the MP, and the binding sites of the RNA were difficult to identify, we bookmarked all paths which started and finished within the eight fivefold axes closest to MP.

If the majority of the potential binding sites are occupied by a PS in every particle, as is expected, for example, if such contacts are vital in triggering a conformational change in the protein building block with which they are in complex, then this path has the mathematical properties of a Hamiltonian path.

Moreover, it is possible that only a fraction of the potential binding sites are occupied by PSs.

In this case, the constraint set corresponds to all paths on the polyhedral cage that connect subsets of the potential binding sites corresponding to the number of the PSs: these are therefore also not Hamiltonian paths.

Binding sites are spaced every xd (36nm) distance, based on actin’s highly conserved structure.

A myosin’s duty ratio r is found analytically by modifying past methods [27] and assuming a filament has traveled x distance at time t, with a myosin head having probability p0n(x,t) and pofl(x,t) of attaching and detaching to binding sites , respectively.

probabilityWhen considering k0" as a high rate of attachment that occurs for a myosin head within a spatial proximity xz to a binding site, and that myosins only bind While detached, the probabili-ty of binding Pan over time to as a site passes is:

Except for the DNA binding sites p.R248 and p.R273 in the p53 DNA binding domain, we did not find mutational hotspots in known tumor suppressors that appeared in more than five cancer types.

Recently, computational structural studies have explored mutational effects on specific regions of a protein (e.g., the binding site ) [28—31].

For oncoproteins, of 40 mutational hotspots, 15 (38%) fell at functional sites, including GTP/ ATP binding sites and other active sites of enzymes.

The p.R248 and p.R273 hotspots were within the DNA binding site , and have each been reported as sites of potentially oncogenic mutations in many cancer types, including breast cancer[55].

Here, we collectively analyzed the domain position-based hotspots for K-RAS, H-RAS, and N-RAS, finding that at least one of the GTP binding site residues p.G12 or p.G13, or the active site residue p.R61 show a relatively high mutation rate in at least five cancer types (Fig.

We used the odds ratio and Fish-er’s Exact Test to calculate the tendency of mutational hotspots in oncoproteins to occur at ATP/GTP binding sites or enzyme-active sites, as compared with mutational hotspots in tumor suppressor proteins.

Under the assumption of large receptor protein (large enough to effectively represent the sequences of the diverse receptor universe), obviously searching for all the binding sites or pockets of a particular finite size protein is equivalent to searching for the whole universe of the receptors.

Therefore, in this case, probing interactions can be reached approximately equivalently by the following three approaches: (1) multiple ligands binding to the same receptor, or (2) multiple receptors binding to the same ligand, or (3) a ligand binding to a receptor exploring the different binding sites (modes).

From the above equilvalence discussions in terms of probing the interactions of molecular recognition, another way of quantify the specificity is to find the discrimination in binding affinities of a ligand binding with different binding sites of a receptor.