Publications by authors named "Srividya Mohan"

Ribosomes are remarkable ribonucleoprotein complexes that are responsible for protein synthesis in all forms of life. They polymerize polypeptide chains programmed by nucleotide sequences in messenger RNA in a mechanism mediated by transfer RNA. One of the most challenging problems in the ribosome field is to understand the mechanism of coupled translocation of mRNA and tRNA during the elongation phase of protein synthesis.

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During protein synthesis, mRNA and tRNAs must be moved rapidly through the ribosome while maintaining the translational reading frame. This process is coupled to large- and small-scale conformational rearrangements in the ribosome, mainly in its rRNA. The free energy from peptide-bond formation and GTP hydrolysis is probably used to impose directionality on those movements.

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The L1 stalk of the large ribosomal subunit undergoes large-scale movements coupled to the translocation of deacylated tRNA during protein synthesis. We use quantitative comparative structural analysis to localize the origins of L1 stalk movement and to understand its dynamic interactions with tRNA and other structural elements of the ribosome. Besides its stacking interactions with the tRNA elbow, stalk movement is directly linked to intersubunit rotation, rotation of the 30S head domain and contact of the acceptor arm of deacylated tRNA with helix 68 of 23S rRNA.

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During ribosomal translocation, a process central to the elongation phase of protein synthesis, movement of mRNA and tRNAs requires large-scale rotation of the head domain of the small (30S) subunit of the ribosome. It has generally been accepted that the head rotates by pivoting around the neck helix (h28) of 16S rRNA, its sole covalent connection to the body domain. Surprisingly, we observe that the calculated axis of rotation does not coincide with the neck.

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In RNA, A-form helices are commonly terminated by tetraloops or 3' dangling ends. Aside from helices themselves, these helix-breaking motifs appear to be among the most frequent and repetitive structural elements of large folded RNAs. We show here that within a frequent type of tetraloop, cGNRAg (G is guanine, N is any base, R is purine, A is adenine), a tension exists between the backbone torsional energy of the loop and the energy contributed by molecular interactions (stacking and pairing).

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The binding of proteins to specific sequences of DNA is an important feature of virtually all DNA transactions. Proteins recognize specific DNA sequences using both direct readout (sensing types and positions of DNA functional groups) and indirect readout (sensing DNA conformation and deformability). Previously we showed that the P22 c2 repressor N-terminal domain (P22R NTD) forces the central non-contacted 5'-ATAT-3' sequence of the DNA operator into the B' state, a state known to affect DNA hydration, rigidity and bending.

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The conversion of a nucleic acid from single strands to double strands is thought to involve slow nucleation followed by fast double-strand propagation. Here, for RNA double-strand propagation, we propose an atomic resolution reaction mechanism. This mechanism, called the stack-ratchet, is based on data-mining of three-dimensional structures and on available thermodynamic information.

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We describe a method to establish chronologies of ancient ribosomal evolution. The method uses structure-based and sequence-based comparison of the large subunits (LSUs) of Haloarcula marismortui and Thermus thermophilus. These are the highest resolution ribosome structures available and represent disparate regions of the evolutionary tree.

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New structural analysis methods, and a tree formalism re-define and expand the RNA motif concept, unifying what previously appeared to be disparate groups of structures. We find RNA tetraloops at high frequencies, in new contexts, with unexpected lengths, and in novel topologies. The results, with broad implications for RNA structure in general, show that even at this most elementary level of organization, RNA tolerates astounding variation in conformation, length, sequence and context.

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Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification.

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