Advanced Protein Secondary Structure Prediction Server allows us to predict the secondary structure of proteins from their amino acid sequence. This is an advanced version of PSSP server, which participates in CASP3 and in CASP4. This server is also part of Meta II Prediction server.
Secondary structure prediction is a set of techniques in bioinformatics that aim to predict the local secondary structures of proteins based only on knowledge of their amino acid sequence. The accuracy of current protein secondary structure prediction methods is assessed in weekly benchmarks such as LiveBench and EVA.
The alignment of the H bonds in the alpha helix creates a dipole moment for the helix with a resulting partial positive charge at the amino end of the helix. Because this region has free NH2 groups, it will interact with negatively charged groups such as phosphates. The most common location of α helices is at the surface of protein cores, where they provide an interface with the aqueous environment.
β sheets are formed by H bonds between an average of 5–10 consecutive amino acids in one portion of the chain with another 5–10 farther down the chain. The interacting regions may be adjacent, with a short loop in between, or far apart, with other structures in between. Every chain may run in the same direction to form a parallel sheet, every other chain may run in the reverse chemical direction to form an antiparallel sheet, or the chains may be parallel and anti parallel to form a mixed sheet.
The best modern methods of secondary structure prediction in proteins reach about 80% accuracy; this high accuracy allows the use of the predictions as feature improving fold recognition and ab initio protein structure prediction, classification of structural motifs, and refinement of sequence alignments.
Early methods of secondary structure prediction, introduced in the 1960s and early 1970s, focused on identifying likely alpha helices and were based mainly on helix-coil transition models. Significantly more accurate predictions that included beta sheets were introduced in the 1970s and relied on statistical assessments based on probability parameters derived from known solved structures. These methods, applied to a single sequence, are typically at most about 60-65% accurate and often under-predict beta sheets. The evolutionary conservation of secondary structures can be exploited by simultaneously assessing many homologous sequences in a multiple sequence alignment, by calculating the net secondary structure propensity of an aligned column of amino acids. In concert with larger databases of known protein structures and modern machine learning methods such as neural nets and support vector machines, these methods can achieve up to 80% overall accuracy in globular proteins. The theoretical upper limit of accuracy is around 90%, partly due to idiosyncrasies in DSSP assignment near the ends of secondary structures, where local conformations vary under native conditions but may be forced to assume a single conformation in crystals due to packing constraints. Limitations are also imposed by secondary structure prediction’s inability to account for tertiary structure; for example, a sequence predicted as a likely helix may still be able to adopt a beta-strand conformation if it is located within a beta-sheet region of the protein and its side chains pack well with their neighbors. Dramatic conformational changes related to the protein’s function or environment can also alter local secondary structure.
The nearest neighbor method of secondary structure prediction has also been called memory-based, exemplar-based, or the homologous method. The method is performed by finding some number of the closest sequences (from a database of proteins with known structure) to a subsequence defined by a window around the amino acid of interest. Using the known secondary structures of the aligned sequences (generally weighted by their similarity to the target sequence) a secondary structure prediction is made. Sequences are chosen based on their similarity
The nearest neighbor method relies on selecting the closest subsequences to a window around the amino acid which is being predicted. Of course, this can be done in a number of ways.