ions, pH dependency, and Brownian dynamics, which are playing significant role in
Free energy of binding to receptor. Many relevant receptors are not crystallized yet,
it is clearly evident that, errors occurring in in silico model structure and plurality of
interactions with the binding site play a dominant role in correctly identify any
novel inhibitor. Prior knowledge of physico-chemical interactions at active site and
the functional importance of interacting residues influence the pose of binding of
inhibitors to flexible receptors. A prior knowledge about the mechanism of binding
provides lead towards the accuracy and effective binding of docked ligand. Flexible
peptides derived structures provide higher affinity and in future, emerging field of
study will be designing of such restrained chemicals driven by highly active peptides. Free energy estimation, rather than scoring (however accurate it may be),
provides better designing capability. Knowledge of mechanism of inhibition is
mandatory for innovation of novel chemical structure to lead the drug design, even
in dominant era of artificial intelligence.
In conclusion, we have attempted to highlight the existing challenges in estimating the ligand receptor binding and critically inspect the methods applied day in
and day out in the field of structure-based drug design. Summarization of tools and
case studies are not the scope of the review. Most important aspect is that this field
evolved largely using efficient algorithm and computational tools, however, effective use requires more indulgence of chemistry and biology, in future to progress
successfully.
Acknowledgements We sincerely thank the facilities under Center of Excellence and Builder
project, Department of Biotechnology (DBT), for supporting the computational work and DST
purse for software support. SKP is supported by DBT-BINC fellowship. The authors thank all
group members and Pawan Kumar for valuable input on improving the manuscript.
References
1. Seifert MHJ, Wolf K, Vitt D (2003) Virtual high-throughput in silico screening. Biosilico
1:143–149. https://doi.org/10.1016/s1478-5382(03)02359-x
2. Engin H, Gursoy A, Nussinov R, Keskin O (2014) Network-based strategies can help monoand poly-pharmacology drug discovery: a systems biology view. Curr Pharm Des 20:1201–
1207. https://doi.org/10.2174/13816128113199990066
3. Jones PG (1984) Crystal structure determination: a critical view. Chem Soc Rev 13:157–
172. https://doi.org/10.1039/cs9841300157
4. Billeter M, Wagner G, Wüthrich K (2008) Solution NMR structure determination of proteins
revisited. J Biomol NMR 42:155–158. https://doi.org/10.1007/s10858-008-9277-8
5. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of
search algorithms and a guide to scoring functions. Proteins Struct Funct Genet 47:409–443.
https://doi.org/10.1002/prot.10115
6. Marrone TJ, Briggs JM, McCammon JA (1997) Structure-based drug design: computational
advances. Annu Rev Pharmacol Toxicol 37:71–90. https://doi.org/10.1146/annurev.
pharmtox.37.1.71
7. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and
scoring methods on systems of pharmaceutical relevance. Proteins Struct Funct Genet
56:235–249. https://doi.org/10.1002/prot.20088
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
161
Free energy of binding to receptor. Many relevant receptors are not crystallized yet,
it is clearly evident that, errors occurring in in silico model structure and plurality of
interactions with the binding site play a dominant role in correctly identify any
novel inhibitor. Prior knowledge of physico-chemical interactions at active site and
the functional importance of interacting residues influence the pose of binding of
inhibitors to flexible receptors. A prior knowledge about the mechanism of binding
provides lead towards the accuracy and effective binding of docked ligand. Flexible
peptides derived structures provide higher affinity and in future, emerging field of
study will be designing of such restrained chemicals driven by highly active peptides. Free energy estimation, rather than scoring (however accurate it may be),
provides better designing capability. Knowledge of mechanism of inhibition is
mandatory for innovation of novel chemical structure to lead the drug design, even
in dominant era of artificial intelligence.
In conclusion, we have attempted to highlight the existing challenges in estimating the ligand receptor binding and critically inspect the methods applied day in
and day out in the field of structure-based drug design. Summarization of tools and
case studies are not the scope of the review. Most important aspect is that this field
evolved largely using efficient algorithm and computational tools, however, effective use requires more indulgence of chemistry and biology, in future to progress
successfully.
Acknowledgements We sincerely thank the facilities under Center of Excellence and Builder
project, Department of Biotechnology (DBT), for supporting the computational work and DST
purse for software support. SKP is supported by DBT-BINC fellowship. The authors thank all
group members and Pawan Kumar for valuable input on improving the manuscript.
References
1. Seifert MHJ, Wolf K, Vitt D (2003) Virtual high-throughput in silico screening. Biosilico
1:143–149. https://doi.org/10.1016/s1478-5382(03)02359-x
2. Engin H, Gursoy A, Nussinov R, Keskin O (2014) Network-based strategies can help monoand poly-pharmacology drug discovery: a systems biology view. Curr Pharm Des 20:1201–
1207. https://doi.org/10.2174/13816128113199990066
3. Jones PG (1984) Crystal structure determination: a critical view. Chem Soc Rev 13:157–
172. https://doi.org/10.1039/cs9841300157
4. Billeter M, Wagner G, Wüthrich K (2008) Solution NMR structure determination of proteins
revisited. J Biomol NMR 42:155–158. https://doi.org/10.1007/s10858-008-9277-8
5. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of
search algorithms and a guide to scoring functions. Proteins Struct Funct Genet 47:409–443.
https://doi.org/10.1002/prot.10115
6. Marrone TJ, Briggs JM, McCammon JA (1997) Structure-based drug design: computational
advances. Annu Rev Pharmacol Toxicol 37:71–90. https://doi.org/10.1146/annurev.
pharmtox.37.1.71
7. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and
scoring methods on systems of pharmaceutical relevance. Proteins Struct Funct Genet
56:235–249. https://doi.org/10.1002/prot.20088
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
161