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How Molecular Docking and Virtual Screening Aids in Combating Genetic Disorders

Genetic disorders are the diseases caused by any change in whole or part of the DNA sequence resulting it away from the normal sequence. Genetic disorders can be caused by a single mutation in one gene; mutations in multiple genes; mutations in a combination of gene and environmental factors; or damage to chromosomes, i.e., changes in the number or structure of entire chromosomes, the structures that carry genes.

As the secrets of the human genome are getting unlocked, we are learning that nearly all diseases have a genetic component. Some diseases are caused by mutations that are inherited from the parents and are present in an individual at birth, e.g., sickle cell anemia.

Other diseases might be resulted from acquired mutations in a gene or group of genes that occur during a person's life. Such mutations are not inherited from a parent, but occur either randomly or due to some environmental exposure, i.e., cigarette smoke. These include many cancers, as well as some forms of neurofibromatosis.

Some prominent genetic disorders include:

  • Autism

  • Breast cancer

  • Cystic fibrosis

  • Down’s Syndrome

  • Hemophilia

  • Huntington’s disease

  • Parkinson’s disease

  • Phenylketonuria

  • Thalassemia

Importance of Molecular Docking in Computational Drug Discovery

It has become immensely difficult to conduct experiments directly on living organisms due to various issues related to costly experimentation and ethical laws. In this context, in silico approaches are considered to be much more successful and effective tools in the hunt of curing diseases. With the advent of NGS, enormous amount of raw sequenced data is continually emerging, revealing essential information about the genetic disorders and molecular docking is considered to be the key technique (in silico) for designing novel drugs using automated simulations and approaches.

Computerized drug discovery pipelines are being utilized by the researchers for:

  • Modeling the target proteins into their 3D structures (in case there’s no experimentally determined structure available for the target protein) via homology modeling, threading or ab initio techniques and their respective tools.

  • Making use of molecular docking via huge libraries of compounds.

  • Achieving the stabilization of the target-ligand complex via molecular dynamic simulations.

Steps for the Selection of a Particular Drug Candidate

1. Selection of a suitable drug target - The first step in molecular docking is choosing a drug target. Any macromolecule can be used as a target. Some common targets include proteins, enzymes and regulatory elements. For example BRCA2, a coding gene responsible for causing breast cancer.

2. Structure determination - Next, the three-dimensional structure must be determined or predicted. High resolution structures can be determined using X-rays, NMR, or electron microscopy (EM). Thousands of popular targets have experimentally determined structures available on the protein data bank (PDB). If the target protein’s structure has not yet been determined using experimental approaches, there are various automated tools available to predict their structures, utilizing different structure prediction algorithms working behind them, such as homology modeling, threading/fold recognition, and ab initio. Following is the crystalline structure of EMSY protein encoded by the BRCA2 gene.

3. Binding-site prediction - Many drug targets have known binding sites; if not, software that can predict potential binding sites for different ligands have been developed. Various tools and databases are available to predict the binding sites within a target protein, e.g., RaptorX, BSpred, MOE, etc.

The RaptorX server was used to predict 4 binding pockets in the EMSY protein having 1 domain, 1.85e-04 P-value, 81(80) value for uGDT(GDT) and 92(90) value for uSeqId(SeqId), while 23% of the positions are predicted to be disordered. Following image represents the amino acid residues predicted as the active site residues, present in the 1st binding pocket:

4. Virtual screening - This step refers to identifying novel ligands from a library of compounds with molecular docking, which provides an extremely useful, but time consuming, method of drug discovery because molecules can be designed to have high binding affinity to a very specific site.

5. Molecular dynamics simulations - Docking studies are often validated using further computational methods, i.e., molecular dynamic simulation. It is an essential step for automated drug discovery and designing process which involves the generation of atomic trajectories of a system using numerical integration of Newton’s laws of motion to define specific interatomic potential using the initial condition and boundary condition. The principle behind this approach is the use of a computational method for calculating the time dependent behaviour of a molecular system. Once docking has been performed, it provides detailed information about the fluctuations and conformational changes in proteins and nucleic acids.

6. Trial and testing - The most successful candidates from computational trials can be tested in vitro or in vivo, and eventually progress to clinical trials.

Factors Affecting the Effectiveness of Molecular Docking

Various factors are responsible for the effectiveness of molecular docking for the selection of a particular drug candidate, which includes:

  • Scoring function, which assists in precisely and correctly identifying the most energetically favorable binding poses.

  • Searching algorithm, which assists in thoroughly and efficiently exploring possible positions, orientations and conformations of potential drugs and the target proteins.

  • Availability and quality of an experimentally determined or predicted structure of the target protein.

  • Identification of potential binding sites.

  • Conformational changes of the target proteins after the drug binding.

Effective Drug Candidates Designed by using Computational Approaches

In the past few years, with the development of new technologies and with the advent of new automated and reliable tools, various drug candidates have been designed using molecular docking and molecular dynamics simulations which aids in fighting against various genetic diseases.

Identification of Medicine for Leukemia

The cancer of blood caused by a rise in the number of white blood cells in the human body, is known as ‘Leukemia’. Those white blood cells crowd out the red blood cells and platelets that the body needs to be healthy. The extra white blood cells do not work properly.

An important case study in rational drug design is Imatinib, a tyrosine kinase inhibitor designed particularly for the bcr-abl fusion protein, a characteristic for Philadelphia chromosome-positive leukemias, including chronic myelogenous leukemia and occasionally acute lymphocytic leukemia. Imatinib is a significantly different drug from previous ones for cancer, since most agents of chemotherapy usually target rapidly dividing cells, without differentiating between cancer cells and other tissues.

Identification of Medicine for Cancer

One of the most devastating and destructive diseases include cancer, that is known to be a persistent threat for public health. As of the year 2016, cancer is the second leading cause of death in the United States. There were an estimated 1,685,210 new cases and 595,690 deaths resulting from cancer. Since, many cancer cells lack molecular targets which makes it extremely difficult for anticancer chemotherapeutics to be fully effective.

Toxicity against normal tissues can develop from anticancer therapy, which leads to unwanted side effects. Due to the adverse effects, many anticancer chemotherapeutics are given at suboptimal doses which typically results in failure of therapy, drug resistance, and metastatic diseases. The complications associated with cancer demonstrate the critical need for the development of new anticancer therapies that are successful with minimal unwanted side-effects and reactions.

For this task to be fulfilled, many researchers are turning to in silico methods to accelerate the process. Molecular docking is one of the most promising approach in this regard, providing popular and reliable softwares for drug discovery, design, and repurposing. Researchers have been utilizing molecular docking in cancer research because it provides great insight into protein-ligand interactions, ligand binding mechanisms, and knowledge of the optimal orientation of the ligand bound to its target in order to establish the most stable and optimal complex.

Molecular docking is a crucial computational method that has demonstrated a promising future for the evolution of more effective and potent anticancer therapies. Researchers have discovered, designed and repositioned various drugs against different drug targets of cancer, which includes:

  • PI-083, MG132, and peptide aldehydes are the drug candidates docked against the proteasome (target protein of cancer) using the Glide and GOLD softwares for docking.

  • ZINC03363328, ZINC08828920, ZINC12941947, ZINC03622539, and ZINC1665054 are the drug candidates docked against the Carbonic anhydrases IX (target protein of cancer) using the AutoDock software for docking.

  • AMPPNP and Dacomitinib are the drug candidates docked against the EGFR (target protein of cancer) using the AutoDock and jMetalCpp softwares for docking.

  • Chlorpromazine has been repositioned as an inhibitor against the CcO (target protein of cancer) using the Glide software for docking.

Other genetic diseases for which the computational drug designing and molecular docking algorithms are considered to be promising techniques for developing, designing, discovering and repositioning new drugs include the neurological diseases such as Alzhiemer’s diseases and Parkinson’s disease. Since neurological pathways are relatively complex phenomena of nature to understand, researchers are now utilizing docking approaches and molecular dynamics simulation approaches to design new effective drugs to combat such neurological diseases.

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