(HSA) and compared it with mouse serum albumin (MSA) [75], starting from the
crystal structures of HSA and ATSP-7041 in complex with MDMX. They used 50 ns
molecular dynamics simulations to sample conformational states of HSA; simulation
trajectories were clustered to give five clusters, and in these six (five cluster representatives and one crystal structure) HSA conformations were used for further
docking studies. ATSP-7041 were fully blindly docked to above six HSA conformations using protein–peptide docking tool pepATTRACT [76] and generated
ensemble (*24,000 poses) of possible docking poses for each; then these ensemble of
poses was clustered using k-means algorithm to result 40 clusters for each of six HSA
conformations. Further, they refined each of the 40 clusters representative poses for
each of six HSA conformations and then performed MD simulation for 5 ns to assess
the stability of the pose. Their study resulted four binding sites R1, R2, R3, and R4
which were most occupied and considered for further study. Moreover, representative
poses of ATSP-7041 and HSA complex one for each site was simulated using explicit
solvent, and binding affinity was estimated using MM-GBSA method. However, for
MSA, no crystal structure was available, so they modeled it using swiss model
choosing HSA as template. ATSP-7041 was kept in MSA at sites R1, R2, R3, and R4,
and three replicates of 100 ns MD simulation in explicit solvent were performed.
Their analysis of these results suggested that sites R2 and R3 were not stable for mouse
in contrast to human which they attributed to sequence dissimilarity at the region in
human and mouse serum albumins. Moreover, they also found that sites R1 and R4
have lesser affinity in case of mouse for ATSP-7041 serum albumin binding than
HSA. They also predicted a list of residues in the binding pocket contribution to the
difference in binding energy. The binding site R1 is canonical binding site overlaps
with already known site called Sudlow’s site II, but R4 appears to be a novel binding
site. Such in silico studies try to provide computational protocols which can be
carefully utilized to gain mechanistic detail into protein–ligand interaction processes.
Flexible ligands, e.g., peptides, can show better complementarity by conformational
adaptation to attain several weak interactions with the receptor [77]. Potential to gain
affinity through modulation of flexibility of ligands has been sensed, and nowadays,
smaller peptides are also being evaluated by researchers across the globe for their
therapeutic usage.
2.4 Protein Flexibility During Binding
Proteins are generally flexible molecules. Therefore, flexibility of the receptor has to be
accounted in in silico binding affinity prediction studies to better represent the
physicochemical conditions. The enormous conformational space available to proteins
is very challenging to exhaust in docking studies because of unrealistic sampling
requirements. However, non-exhaustive but simplistic and computationally less
demanding methods have been developed over the years as proxy for accounting the
flexibility of the protein during the binding which can broadly be put in four classes: soft
docking, side chain rotation, molecular relaxation, and docking to multiple structures.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
123
crystal structures of HSA and ATSP-7041 in complex with MDMX. They used 50 ns
molecular dynamics simulations to sample conformational states of HSA; simulation
trajectories were clustered to give five clusters, and in these six (five cluster representatives and one crystal structure) HSA conformations were used for further
docking studies. ATSP-7041 were fully blindly docked to above six HSA conformations using protein–peptide docking tool pepATTRACT [76] and generated
ensemble (*24,000 poses) of possible docking poses for each; then these ensemble of
poses was clustered using k-means algorithm to result 40 clusters for each of six HSA
conformations. Further, they refined each of the 40 clusters representative poses for
each of six HSA conformations and then performed MD simulation for 5 ns to assess
the stability of the pose. Their study resulted four binding sites R1, R2, R3, and R4
which were most occupied and considered for further study. Moreover, representative
poses of ATSP-7041 and HSA complex one for each site was simulated using explicit
solvent, and binding affinity was estimated using MM-GBSA method. However, for
MSA, no crystal structure was available, so they modeled it using swiss model
choosing HSA as template. ATSP-7041 was kept in MSA at sites R1, R2, R3, and R4,
and three replicates of 100 ns MD simulation in explicit solvent were performed.
Their analysis of these results suggested that sites R2 and R3 were not stable for mouse
in contrast to human which they attributed to sequence dissimilarity at the region in
human and mouse serum albumins. Moreover, they also found that sites R1 and R4
have lesser affinity in case of mouse for ATSP-7041 serum albumin binding than
HSA. They also predicted a list of residues in the binding pocket contribution to the
difference in binding energy. The binding site R1 is canonical binding site overlaps
with already known site called Sudlow’s site II, but R4 appears to be a novel binding
site. Such in silico studies try to provide computational protocols which can be
carefully utilized to gain mechanistic detail into protein–ligand interaction processes.
Flexible ligands, e.g., peptides, can show better complementarity by conformational
adaptation to attain several weak interactions with the receptor [77]. Potential to gain
affinity through modulation of flexibility of ligands has been sensed, and nowadays,
smaller peptides are also being evaluated by researchers across the globe for their
therapeutic usage.
2.4 Protein Flexibility During Binding
Proteins are generally flexible molecules. Therefore, flexibility of the receptor has to be
accounted in in silico binding affinity prediction studies to better represent the
physicochemical conditions. The enormous conformational space available to proteins
is very challenging to exhaust in docking studies because of unrealistic sampling
requirements. However, non-exhaustive but simplistic and computationally less
demanding methods have been developed over the years as proxy for accounting the
flexibility of the protein during the binding which can broadly be put in four classes: soft
docking, side chain rotation, molecular relaxation, and docking to multiple structures.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
123