Non Syndromic Mental Retardation Biology Essay

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Mental retardation is a Neuro-developmental disorder characterized by significantly sub average general intellectual functioning associated with impairments in adaptive behavior and manifested during the developmental period (Molinario, et al, 2002). Mental Retardation is a highly heterogeneous condition with a prevalence of approximately 2 % in general population (Mir, et al, 2009).

Non syndromic mental retardation is not associated with embossing clinical traits and symptoms where as Syndromic MR is associated with abnormalities, dysomorphology or other clinical implications.

Till now six genes have been identified in various ethnic populations in which genetic aberrations or mutations in the gene sequence have been identified for causing the condition. (Mir, et al, 2010) The anomalies lead to truncation of the proteins involved in pathways that are responsible for certain important functions related to adaptive behavior, learning response and memory storage. Brief descriptions of the proteins that are truncated or face structural anomalies due to mutation are as follows.

1.2 CRBN [MIM:607417]

. CRBN is a gene coding for cereblon. It is a Component of SCF protein ligase complex, a complex that mediates the ubiquitination and subsequent proteosomal degradation of target proteins and is required for limb outgrowth and expression of the fibroblast growth factor FGF8. In the complex, it may act as a substrate receptor. It regulates the assembly and neuronal surface expression of large-conductance calcium-activated potassium channels in brain regions involved in memory and learning via its interaction with KCNT1. Defects in CRBN are the cause of mental retardation autosomal recessive type 2A (MRT2A). MRT2A patients display mild mental retardation with a standard IQ ranged from 50 to 70. IQ scores are lower in males than females. Developmental milestones are mildly delayed. There are no dysmorphic or autistic features (Betchtel et al, 2002).

1.3 CC2D1A [MIM:608443]

CC2D1A is a transcription factor that binds specifically to the FRE (five repressor element) and represses HTR1A gene transcription in neuronal cells. The combination of calcium and ATP specifically inactivates the binding with FRE. It may play a role in the altered regulation of HTR1A associated with anxiety and major depression. It mediates HDAC-independent repression of HTR1A promoter in neuronal cells. Defects in CC2D1A are the cause of mental retardation autosomal recessive type 3 (MRT3). Patients display severe mental retardation and psychomotor development delay in early childhood (Vanagaite et al, 2005).

1.4 GIRK2 [MIM:611092](Glutamate Receptor Ionotrop Kinate 2)

GIRK2 is an Ionotropic glutamate receptor. L-glutamate acts as an excitatory neurotransmitter at many synapses in the central nervous system. Binding of the excitatory neurotransmitter L-glutamate induces a conformational change, leading to the opening of the cation channel, and thereby converts the chemical signal to an electrical impulse. The receptor then desensitizes rapidly and enters a transient inactive state, characterized by the presence of bound agonist. It may be involved in the transmission of light information from the retina to the hypothalamus. It modulates cell surface expression of NETO2. Defects in GRIK2 are the cause of mental retardation autosomal recessive type 6 (MRT6). It is characterized by significantly sub-average general intellectual functioning associated with impairments in adaptive behavior and is manifested during the developmental period. In contrast to syndromic or specific mental retardation which also presents with associated physical, neurological and/or psychiatric manifestations, intellectual deficiency is the only primary symptom of non-syndromic mental retardation. MRT6 patients display mild to severe mental retardation and psychomotor development delay in early childhood. Patients in this condition do not have neurologic problems, congenital malformations, or facial dysmorphism. Body height, weight, and head circumference are normal (Motazacker et al, 2005).

1.5 PRSS12 [MIM:249500]

Neurotrypsin is coded by the gene PRSS12. It has a role in neuronal plasticity and the proteolytic action may sub serve structural reorganizations associated with learning and memory operations. Defects in PRSS12 are the cause of mental retardation autosomal recessive type 1. Mental retardation is a mental disorder characterized by significantly sub-average general intellectual functioning associated with impairments in adaptive behavior and manifested during the developmental period (Molinari et al, 2003).

1.6 TUSC3 (Tumor Suppressor Candidate 3) [MIM:611093]

It may be involved in N-glycosylation through its association with N-oligosaccharyl transferase. Defects in TUSC3 are the cause of mental retardation autosomal recessive type 7 (MRT7); also known as mental retardation non-syndromic autosomal recessive 7. (Molinari F, et al, 2008) (Garshasbi M, et al, 2008 )

1.7 TRAPPC 9 (Trafficking Protein particle Complex 9)

It functions as an activator of NF-kappa-B through increased phosphorylation of the IKK complex. It may also function in neuronal cell differentiation. It may also play a role in vesicular transport from endoplasmic reticulum to Golgi (Pendergast et al 2009).Mutation in TRAPPC9 is responsible for causing Non syndromic mental retardation (Mir et al 2009).


Proteins are responsible for carrying out all the important functions of the cell and living systems. The three dimensional conformation of a protein depends upon the sequence of amino acids. Their arrangement determines a protein’s hydrophilic or hydrophobic properties thus determining its function and other physiochemical properties. Bioinformatics has revolutionized 3-D structure prediction. Structural Bioinformatics provides tools which take the primary set of sequence of amino acids as input to determine or predict a proteins 3-D structure. The structure could also be predicted on the basis of homology between different protein sequences. Molecular modeling involves using theoretical methods and computational techniques to model organic molecules in silico.

Figure 1.1: Showing ball and stick representation of 3D conformation of a protein along secondary structure motifs of a generic protein. (Adapted from )


Homology Modeling, also known as comparative modeling, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "template"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence (fig 1.2). The sequence alignment and template structure are then used to produce a structural model of the target. Because protein structures are more conserved than DNA sequences, detectable levels of sequence similarity usually imply significant structural similarity. Till this point, there is still a question mark on the predicted structure regarding the structure’s validity. To resolve this complication various online servers are only dedicated to check and validate protein folding, conformation and structure based on heuristic and statistical inference .i.e. WHATIF, ANOELA, RAMPAGE, PROSA etc. The key to a good and stable structure is that it should have minimum free energy with a hydrophobic interior and hydrophilic residues present at the exterior of the protein. Figure 1.2 demonstrates a flow chart of the steps that are followed in Homology modeling.

Figure 1.2: Showing a flow chart diagram showing various steps involved in homology modeling


Ab initio methods are another way of predicting protein structures. They use statistics as a basis of determining folds, alpha helixes, beta sheets, and coils - mainly secondary structures. Neural Networks, Chou Fasman and Hidden Markov models are examples of some ab initio prediction methods. Ab initio methods are not the most accurate and reliable methods for modeling proteins owing to limitations in the statistical calculations. The methods could be used to predict structures of small peptides of around 50-60 amino acids long with high accuracy and reliability. Large molecular structures cannot be accurately predicted using this methodology but there is a lot of scope in improving ab inito methods for structure prediction.


Protein interactions are responsible for a wide array of functions in living systems. Proteins interact at almost all levels of cellular organization may it be signaling or transportation, either intercellular or intracellular. Some protein interactions are responsible for proper folding, e.g. chaperons. Protein interactions are also responsible for transportation of other proteins to the right cellular destination, e.g. leader peptides are responsible for transportation of molecules from exterior environment to the cytoplasm. This process may lead to conformational changes in the protein structure. Thus, studying Protein-Protein Interactions are of great significance in understanding and deciphering biological function and pathology. Figure 1.3 depicts a typical interactome involving thousands of proteins and their multiple interactions.

Figure 1.3: Showing a complex interactome of proteins in which proteins are represented as nodes and interaction between them is represented as edges. Adapted from (


Bioinformatics has revolutionized the domain of research in life sciences. It has streamlined and fast tracked research by providing tools and techniques for effective, accurate data management, annotation and analysis. Molecular modeling and 3D visualization of proteins and molecules have given researchers a simulated environment to manipulate and play with protein structures in order to characterize molecules with putative functions. The art of molecular modeling has also revolutionized the world of pharmacogenomics and pharmacoproteomics. Computer aided designs and structures facilitate in identifying potential drug binding sites, identifying protein docking sites and potential formation of protein complexes involved in a particular pathway. Some software suites even replicate or simulate exact physiological environment which play a very important role ranging from protein folding to complex interactions. The docking sites are basically identified using mathematical algorithms involving complex calculations, which infer protein binding and then create models on the basis of hydrophobic favored regions and/or electrostatically favored regions. (fig 1.4) Shows a computational docking of two proteins using a protein docking software.

Computational simulation of docking sites and visualization has fast tracked drug designing and all the leading pharmaceuticals have their own customized and protected docking software. The whole simulation serves a very site directed role in designing an effective protocol for the development of a particular drug or therapy. The technology puts medical research on the fore fronts of science for academic and research purposes; there are certain publicly accessible servers and software that facilitate and provide free services.

Figure 1.4: Showing molecular surface interaction between two proteins obtained through a docking software. Adapted from (

With the development of Fourier Transform Conformation (FFT), it was computationally feasible to validate and evaluate millions of docking sites and complex protein interactions using mathematical functions for structures with absolutely no prior information (Stephen et al, 2003). Some software suites used for proteins dockings are Hex, AutoDock, etc There is a series of events held for interaction validation and is known by the name of CAPRI (Critical assessment of Protein Interactions) (Anin et al, 2003). Some online servers are associated with the conference and a list of them is provided below.

Clus Pro










Non syndromic mental retardation is a condition with no clinical implication or symptoms. Therefore, the truncation or the knockout study of proteins in various identified and reported genes could help in understanding of how the brain works in response to creating memories, learning and behavior. Trying to understand what changes occur in the brain when someone learns, how these changes persist over time to support memory, and why some things are easier to remember then others, is not an easy task because learning is not a unitary process. There is not a single mechanism of learning in the brain but instead there are distinct kinds of learning that depend on distinct brain regions. A brain structure called the hippocampus supports memory for facts and events. This is what one relies on to remember someones name or what one had for breakfast, where as another structure called the amygdala supports emotional memory. One can have fear of dogs even if one has lost the explicit hippocampus dependent memory of being bitten by one as a child. So these memory systems are fairly independent. The basal ganglia supports habit memory. This is what we are using when we brush our teeth or drive to work when our mind is elsewhere there cerebral cortex supports perceptual learning. Even basic functions like being able to see depend critically on experience. The cerebellum supports motor learning. This is the process by which one acquires skilled movements. If we were to zoom in on any one of these brain area’s we would find that they are made up of the same basic building blocks or molecules i.e. proteins.

The molecular structures of these proteins could be modeled and there possible functional and physical interactions could be inferred using the above mentioned bioinformatics techniques. It is difficult to run in vivo tests with human brain at micro level with some exceptions. Neuroinformatics could play its potential role in modeling to mapping these important neuropeptides involved in learning, memory and behavior. Bioinformatics facilitates us with tools to predict, model, visualize and annotate 3-D structures of neuropeptides. We can predict and visualize the interactomes of proteins in important signaling pathways, formation of protein-protein complexes through protein docking, and even identify potential drug sites using the very same technique. We can even design and develop computational simulations to accurately set up protocols for wet lab validation.


A Neurobioinformatics approach would be utilized in remodeling previously predicted structures and modeling newly identified proteins in which mutations are responsible for causing the condition. Modeling Neuropeptides whose truncation is responsible for causing NSMR is a chief aspect of the project. Then, it would be noted, that if they potentially interact and could be more or less part of the same functional pathway so in order to prove, protein docking and domain interactions would be analyzed. The literature provides sufficient physiological or intracellular positions of some proteins which have been experimentally identified, while other localizations would be predicted using Bioinformatics. This could potentially be an important cognitive pathway in the human brain. Utilizing prior knowledge and amalgamating it with our acquired knowledge some events important to neuronal or synaptic plasticity are to be mapped.