General Information


Christopher Rusnak



CoRE 637


Rutgers University



Faculty Advisor:

Yana Bromberg, Biochemistry and Microbiology, Rutgers University


Protein Activity Unmasked: Using Computational Analysis to Find Functional Sites in Proteins

Project Description

Proteins are strings of molecules, each of which is known as an amino acid, that are transcribed and translated from DNA, and are implicated in a number of functions within an organism, such as various chemical reactions and expressing genes. In many cases, whenever there is a change at the level of DNA, there is a change at the level of the protein, which may affect that protein’s function, structure, or both. One type of mutation that can have a significant effect on these properties is known as a non-synonymous single nucleotide polymorphism (nsSNP), in which one unit of DNA, a nucleotide, is substituted for another, resulting in one amino acid in a protein being substituted for another. The overall structure of a protein is the arrangement of the sequence of amino acids and the patterns that they can form in space. The overall function of the protein is determined by this structure and specific amino acids within the sequence of the protein that compose that structure. Out of all of the amino acids in a protein, only a select few are functionally important.


Only experimentation can confirm a protein’s structure, but the methods involved are costly and time-consuming, so only a very small percentage of all proteins have a known structure. For cases in which the protein structure is unknown, my mentor for my summer research project had developed a program known as SNAP (Screening for Non-Acceptable Polymorphisms), which may be helpful in identifying functional amino acids. SNAP takes as its input a protein sequence, as well as positions on that sequence that you choose to mutate. The program then predicts whether or not the mutation will have an effect on overall protein function, and lists the reliability index for each prediction. Negative values imply that the mutation has no effect on function while positive values imply that the mutation does have an effect on function. The higher the absolute value of the entry, the more reliable the prediction. Taking all of this into account, the overall goal of our research is to be able to take a protein at the level of sequence and find specific amino acids that are functionally important. Functional annotation of proteins has its importance because it is useful for finding specific mutations that cause diseases (sickle cell anemia, for example, has its roots in an nsSNP in the hemoglobin beta gene), designing drugs to activate or inhibit certain proteins, gene therapy, and similar issues. The progress of these fields depends on quickly obtaining accurate results, which tools based on bioinformatics such as SNAP may be able to deliver.

Weekly Project Log:

Week 1: Within the first week of the program, I have met with my mentor, discussed the overall goal of the project, read several relevant articles on the topic at hand, and attended several workshops and orientation meetings.

Week 2: I prepared for and presented my first presentation, and I continued my readings, mostly on probabilistic models that are often used in bioinformatics.

Week 3: I looked into several programs whose methodology may be useful for my work, performed combinatorial analysis of protein mutagenesis, and planned out the next stages of the project.

Week 4: This week, I became familiar with Environment Specific Substitution Tables, learned the basics of the Python programming language, and planned out the steps for writing my first program with Python.

Week 5: I became comfortable programming in Python, and I made a series of programs that analyzed a database of protein sequences and corresponding SNAP scores and annotations.

Week 6: I plotted a series of distributions based on the output of my programs, then analyzed and presented my findings to my lab group.

Week 7: I prepared for and presented my final presentation, and I outlined some ideas for how to continue my research.

Week 8: I wrote my research report for the program and continued to plan courses of action for future directions of this project.

Links and Resources

1.      Bromberg et al. "In Silico Mutagenesis: a Case Study of the Melanocortin 4 Receptor." The FASEB Journal 23.9 (2009): 3059-069. Print.

2.      Bromberg et al. "SNAP: Predict Effect of Non-synonymous Polymorphisms on Function." Nucleic Acids Research 35.11 (2007): 3823-835. Print.

3.      DIMACS REU

4.      SNAP Program