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The correct CHOICE is (d) It doesn’t have to be first trained by sequences with known structures

Easy explanation: The neural network has to be first trained by sequences with known structures so it can recognize the amino acid PATTERNS and their relationships with known structures. During this process, the weight FUNCTIONS in hidden layers are optimized so they can relate input to output correctly. When the SUFFICIENTLY trained network PROCESSES an unknown sequence, it applies the rules learned in training to recognize particular structural patterns.

The correct choice is (a) True

The best I can explain: A number of algorithms for IDENTIFYING transmembrane helices have been developed where the EARLY algorithms based their PREDICTION on hydrophobicity scales. They typically scan a window of seventeen to twenty-five residues and assign membrane spans based on hydrophobicity SCORES. Some are ALSO able to determine the orientation of the membrane helices based on the positive-inside rule.

The correct choice is (c) It is not a machine learning process

To explain: A NEURAL network is a machine learning process that requires a structure of multiple layers of interconnected variables or NODES. In secondary structure prediction, the input is an amino acid sequence and the OUTPUT is the probability of a residue to ADOPT a PARTICULAR structure.

Correct CHOICE is (d) They are based on single sequence statistics with clear RELATION to KNOWN protein-folding theories

For EXPLANATION I would say: They are based on single sequence statistics without clear relation to known protein-folding theories. The predictions solely RELY on local sequence information and fail to take into account long range interactions. A Chou-Fasman–based prediction does not even consider the short-range environmental information.

The correct choice is (b) They have an integral repeat of seven residues

The explanation is: Coiled coils have an integral repeat of seven residues (heptads) which assume a side-chain packing geometry at facing residues. For every seven residues, the first and fourth are HYDROPHOBIC, facing the helical interface; the others are hydrophilic and exposed to the solvent. The sequence PERIODICITY FORMS the basis for designing algorithms to predict this important STRUCTURAL DOMAIN.

Right option is (c) This type of method uses the ab initio secondary structure PREDICTION of individual sequences only

To explain I would say: The idea behind this approach is that close protein homologs should adopt the same secondary and TERTIARY structure. When each individual sequence is predicted for secondary structure using a method similar to the GOR method, ERRORS and variations may occur. However, evolutionary conservation DICTATES that there should be no major variations for their secondary structure elements.

The CORRECT option is (B) False

To explain I would say: Prof stands for Protein FORECASTING. It is an ALGORITHM that combines PSI-BLAST profiles and a multistaged NEURAL network, similar to that in PHD. In addition, it uses a linear discriminant function to discriminate between the three states.

Right answer is (b) False

Best EXPLANATION: Predictions solely based on hydrophobicity profiles have high ERROR RATES. As with the third-generation predictions for GLOBULAR proteins, applying evolutionary information with the help of neural networks or HMMs can IMPROVE the prediction accuracy significantly.

Correct choice is (C) It uses a combination of EVOLUTIONARY information only

To elaborate: A profile is extracted from the multiple sequence alignment generated from three rounds of automated PSI-BLAST. The profile is then used as input for a NEURAL NETWORK prediction similar to that in PHD, but without the jury layer. To achieve higher accuracy, a unique filtering algorithm is implemented to filter out unrelated PSI-BLAST hits during profile construction.

Right choice is (d) They are not DRUG targets or receptors

For explanation: The membrane proteins are also of tremendous biomedical importance, as they often SERVE as drug targets for pharmaceutical development. There are two types of integral membrane proteins: α-helical type and β-barrel type. Most transmembrane proteins contain solely α-helices, which are found in the CYTOPLASMIC membrane. A few membrane proteins consist of β-strands FORMING a β- barrel topology, a cylindrical structure composed of ANTIPARALLEL β-sheets.

Right option is (b) This type of method predicts the secondary structure based on a MULTIPLE query sequence

Best explanation: Examples of ab initio prediction are the Chou–Fasman and Garnier, Osguthorpe, Robson (GOR) methods. The ab initio methods were developed in the 1970s when protein STRUCTURAL DATA were very limited. The statistics derived from the limited data sets can therefore be RATHER inaccurate. However, the methods are simple enough that they are often used to ILLUSTRATE the basics of secondary structure prediction.

The correct option is (b) False

The explanation: Multicoil is a web-based program for predicting COILED coils. The scoring matrix is constructed based on a DATABASE of known two-stranded and three-stranded coiled coils. The program is more CONSERVATIVE than Coils. It has been recently used in SEVERAL genome-wide studies to screen for protein–protein interactions mediated by coiled coil domains.

The correct choice is (a) True

To explain I would say: The ab initio methods, which belong to early generation methods, predict secondary structures based on statistical calculations of the residues of a SINGLE query sequence. The homology-based methods do not rely on statistics of residues of a single sequence, but on COMMON secondary structural PATTERNS conserved among multiple HOMOLOGOUS sequences.

The correct answer is (b) A neural network is trained by a SINGLE sequence

Best explanation: A neural network is trained not by a single sequence but by a sequence profile derived from the multiple sequence alignment. This combined approach has been shown to improve the accuracy to above 75%, which is a BREAKTHROUGH in secondary structure prediction. The improvement mainly comes from enhanced secondary structure signals through consensus drawing. The FOLLOWING lists several frequently used THIRD generation prediction algorithms available as web servers.

Correct answer is (b) It is a web-based program that COMBINES neural network only

The best EXPLANATION: It is a web-based program that combines neural network with multiple sequence alignment. After the initial steps, the resulting alignment in the form of a profile is fed into a neural network that contains THREE hidden layers. The first hidden layer MAKES raw prediction based on the multiple sequence alignment by sliding a window of thirteen positions.

The correct answer is (a) True

Explanation: By ALIGNING multiple SEQUENCES, INFORMATION of positional conservation is revealed. This homology based method has helped improve the prediction ACCURACY by another 10% over the second-generation methods.

The correct option is (c) It is not trained to RECOGNIZE transmembrane helical patterns

For explanation: It is trained to recognize transmembrane helical patterns based on a training set of 160 well-characterized helical membrane proteins. The orientation of the α-helices is predicted based on the positive-inside rule. The prediction output RETURNS the number of transmembrane helices, the BOUNDARIES of the helices, and a GRAPHICAL representation of the helices. This program can also be used to SIMPLY distinguish between globular proteins and membrane proteins.

Right answer is (a) True

To EXPLAIN: The individual α-helices twist and WIND around each other to form a coiled BUNDLE structure. The coiled coil conformation is important in facilitating inter- or intra protein interactions. Proteins possessing these STRUCTURAL domains are often involved in TRANSCRIPTION regulation or in the maintenance of cytoskeletal integrity.

The correct option is (a) True

The best EXPLANATION: These programs for predicting transmembrane α-helices are not applicable for this UNIQUE type of membrane PROTEINS. To predict the β-barrel type of membrane proteins, a small number of algorithms have been made available based on NEURAL networks and related techniques.

Right option is (c) The α-helices generally run parallel to the membrane plane

The explanation is: The hydrophobic helices are normally separated by hydrophilic loops with average lengths of fewer than sixty residues. The residues bordering the transmembrane spans are more positively charged. Another feature indicative of the presence of transmembrane SEGMENTS is that residues at the cytosolic side NEAR the hydrophobic anchor are more positively charged than those at the lumenal or periplasmic side. This is KNOWN as the positive-inside RULE.

Correct option is (a) hydrophobic, ACCURACY

Explanation: Predicting transmembrane helices is relatively easy. The accuracy of Some of the best predicting programs, such as TMHMM or HMMTOP, can exceed 70%. To minimize errors, the presence of SIGNAL peptides can be detected using a number of specialized programs and then manually EXCLUDED.

Correct choice is (a) True

The best I can EXPLAIN: It DETERMINES the propensity or intrinsic TENDENCY of each residue using observed frequencies found in protein crystal structures (CONFORMATIONAL values for coils are not considered). For EXAMPLE, it is known that alanine, glutamic acid, and methionine are commonly found in α-helices, whereas glycine and proline are much less likely to be found in such structures.

Right option is (B) False

Easy explanation: The GOR method is based on the “propensity” of each residue to be in one of the TWO conformational states, helix (H), STRAND(E), turn(T), and coil (C). However, instead of using the propensity VALUE from a single residue to predict a conformational state, it takes short-range interactions of neighboring residues into ACCOUNT.