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**Molecular Biology**
***Protein and Nucleic Acid Sequence Analysis*
Advances in biotechnology have produced massive volumes of  biological sequences and their associatedbiological and  bibliographical information.  For example, the current release (87.0)  of GenBank fromNLM's National Center for Biotechnology  Information (NCBI) contains 269,478 sequences from over11,000  different species and consisting of more than 248 million nucleic  acid bases.  Researcherswho discover new sequences search the  database for other sequences that are similar or otherwiserelevant  to their studies.  The biological information associated with similar  sequences may provideclues to the structure and function of the  newly discovered sequences.  Integrated structuralmodeling using  all known sequences and structures in conjunction with powerful  computational andvisualization tools available this year at NCBI  will further enhance this discovery process.  NCBI'sinformation  systems containing data related to molecular biology, biochemistry,  and genetics nowhandle 20,000 searches daily over the Internet. 
http://www.ncbi.nlm.nih.gov/ 
To find similar sequences, the query sequence is compared to every  other sequence in the database. The computationally intensive  dynamic programming algorithm for comparing two sequences  developed bySmith and Waterman has been commonly used.  The  execution time for this algorithm grows exponentiallywith the size  of the database, making it impractical on conventional sequential  computing systems. DCRT has developed a new parallel algorithm  that greatly reduces the search time.  Taking advantageof the fact  that the comparison of the query sequence with each database  sequence can be doneindependently and in parallel, this "static"  algorithm sorts the sequences in the database bydecreasing length,  assigns each sequence in this sorted list to that "bucket" whose  contents havethe smallest total sequence length, and parcels out  the buckets to the processors.  Given a querysequence with 50  bases, execution time was 2.3 minutes on a 128-processor Intel  iPSC/860 versus 3.3hours with just one processor.  This algorithm  was more than twice as fast as a previously developeddynamic load  balancing algorithm. 
***Protein Folding Prediction*
The protein folding prediction problem is to determine the three- dimensional structure of a proteinmolecule given only its amino  acid sequence.  Understanding this three-dimensional structure is needed in studying the function of proteins in living systems and  designing new ones for biologicaland medical purposes.  Amino acid  sequences of proteins are being discovered at an explosive rate,but  experimental procedures for determining their three-dimensional  structure (for example, x-raycrystallography and nuclear magnetic  resonance (NMR) spectroscopy) are slow, costly, and complex.  Theoretical and computational methods can help by predicting  structure from sequence. 
One reason why this problem is unsolved is that the biochemical  rules governing the folding and thestability of proteins are not  known.  If they were known, an algorithm simulating the folding  couldbe implemented.  One alternative available today is an  algorithm that finds the "best" conformationby searching all  possible conformations; however because this space of all possible  conformations ismuch too large to search, the search is restricted  to a lattice.  The restrictions in thislattice-space Monte Carlo  method introduce distortions and consequently imperfect results. 
DCRT is developing parallel algorithms so that more conformations  can be searched and a morerealistic energy function can be  computed.  These algorithms implement computationally intensive strategies for searching a large number of structures and  calculating each one's free energy,particularly the hydrophobic  potential that is proportional to the solvent-accessible surface area (ASA) of the protein molecule.  The dihedral angle space Monte Carlo  (DASMOC) algorithm combinesknowledge-based rules and global  minimization of free energy to search a large and complex energy space in order to find a protein structure in the most stable energy  state.  Current joint DCRT andNIH/NCI activities include  implementing DASMOC on DCRT's Intel and developing parallel search algorithms that can work with DASMOC; DCRT is also developing  parallel methods for ASA calculations. 
Protein folding can generally be considered to occur in three phases  or structures:  a primarystructure in which amino acids are linked  together end-to-end according to its sequence; a secondary structure in which helices and beta sheets are formed; and a tertiary  structure that is the finalfunctional form of the protein.  The  secondary structure of proteins up to a certain size can be determined using NMR.  NIH/NCRR has supported the development of a  new parallel algorithm that usesthe secondary structure obtained  from NMR experiments and amino acid sequence information to calculate a lowest energy structure, which is the predicted tertiary  (stable final form) structure. This algorithm has been tested, and  the predicted results are accurate for a number of proteins whose structure is known. 
***Ribonucleic Acid (RNA) Structure Prediction*
The ribonucleic acid (RNA) molecule serves several roles in nature.   It is chemically very similar toDNA, but the differences are enough  to distinguish its activities from the DNA molecule.  Themolecule is  a key component for protein synthesis, serving as a template for  protein generation(messenger RNA or mRNA) and being part of  associated molecules for the synthesis of the proteins(ribosomal  RNA or rRNA, and transfer RNA or tRNA).  Many viral infections are  also caused by RNAmolecules, including the common cold, human  immunodeficiency virus (HIV), and polio.  The mechanismsfor  infection may vary.  Some may be used like mRNA while others, like  HIV, actually go through areverse transcription process into the  DNA.  The viral packaging usually carries the virus'viability. 
NCI has developed a new experimental "genetic algorithm" for RNA  folding.  This algorithm is highlyparallelizable and rapidly  convergent to solutions in a large conformational search space of  RNAstructures.  It borrows from the processes of biological  evolution using operations such as mutation,recombination and  reproduction, and a selection criterion based on the idea of the  survival of thefittest.  The algorithm has been designed to run on  the MasPar MP-2 massively parallel computer.  Itcomputes 16,384  RNA conformations at each generation.  Using a random but  structured informationexchange at each generation allows it to  iterate towards an optimal solution. 
NCI also developed a parallel dynamic programming algorithm that  generates suboptimal solutions forRNA folding.  The program on the  MasPar MP-2 is capable of rapidly folding RNA sequences that are over 9,000 nucleotides in length.  HIV and the common cold virus are   some examples of largesequences that have been folded with this  algorithm. 
http://www-lmmb.ncifcrf.gov******