And many of those residues can be substituted by others with little to no functional effect.
That a protein’s amino acid sequence determines it’s structure and function does not mean many alternative amino acids can not do the job equally well, which as @John_Harshman also told you about, is revealed by the variants that exist in the human population.
Speaking of proteins more generally, biochemical experiments have routinely revealed that variant sites in proteins (those that aren’t conserved) also have little to no functional effects if substituted. Just to pick an example, check this reference on biochemical characterizations of mutations on a human G-protein coupled receptor:
Here they experimentally characterize the functional effects of almost all possible single amino acid substitutions in all positions on the protein, and the result is revealed in Figure 4c:
The residues colored pale green tolerate all possible amino acid substitutions:
Unsupervised learning reveals functionally relevant groupings of residues
Given that our data spans thousands of mutations across several treatment conditions, we used unsupervised learning methods to reveal hidden regularities within groups of residues’ response to mutation. In particular, we applied Uniform Manifold Approximation and Projection (UMAP) (McInnes and Healy, 2018) to learn multiple different lower dimensional representations of our data and clustered the output with density-based hierarchical clustering (HDBSCAN; Figure 4—figure supplement 1; Campello et al., 2013). We found residues consistently separated into six clusters that exhibit distinct responses to mutation (Figure 4A,B). Clusters 1 and 2 are globally intolerant to all substitutions, whereas Cluster 3 is vulnerable to proline and charged substitutions. Cluster 4 is particularly inhibited by negatively charged substitutions and Cluster five by proline substitutions, while Cluster 6 is unaffected by any mutation.
As the paper also states, the tolerance to mutations of individual positions in the protein well predicted by comparative genetics:
Metrics for sequence conservation and covariation are often used to predict the effects a mutation will have on protein function (Adzhubei et al., 2013; Capra and Singh, 2007; Hopf et al., 2017). Mutational tolerance, the mean activity of all amino acid substitutions per residue at each agonist concentration, is highly correlated to conservation, both across species for the β2AR (Figure 3—figure supplement 1A; Spearman’s ρ = −0.74; 55 orthologs, predominantly mammals but including a few other vertebrates as well as a small number of invertebrate beta-like sequences, identified from the OMA Database, Supplementary file 1), and across all Class A GPCRs (Spearman’s ρ = −0.68; Figure 3A and Figure 3—figure supplement 1B; Altenhoff et al., 2018; Capra and Singh, 2007; Hopf et al., 2017) at EC100.
Results like these are not at all unusual, so the burden of proof is on you to explain why your non-sequitur has any merit. I remind you that you are the one who attempted to derive the conclusion that the very few amino acid differences between proteins shared among human and chimp must have a functional consequence simply because the majority of proteins have a tiny handful of such differences.