The phrase "alignment to predict" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or any situation where one is discussing the relationship between alignment and prediction outcomes.
Example: "The model's alignment to predict future trends is crucial for accurate forecasting."
Alternatives: "correlation for prediction" or "alignment for forecasting".
All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence.
For general types of cancers, Hosur et al. [ 3] combined linear programming formulation for interface alignment to predict cancer related PPIs.
The Meta-Functional Signature score (MFS) was used in conjunction with multiple sequence alignment to predict functional sites and functional similarity between Hmd enzymes and paralogs.
We further applied the Multi-Harmony (Brandt et al. 2010) method to this alignment to predict the best candidates for residues that determine the functional specificity of the push and pull groups.
The pipeline utilises sequence similarity to ncRNA databases (blast), structure similarity to Rfam (RaveNnA) as well as multiple alignments to predict conserved novel putative RNA structures (RNAz).
We first aligned individual families using the MUSCLE, KALIGN and PCMA programs and used the resulting alignments to predict secondary structure using the JPRED program.
This program employs multiple sequence alignments to predict the helical, beta-sheet, or coiled-coil nature of each position in the primary sequence of a group of proteins.
Many of the genes whose expression is significantly different between L1 and iL3s stages are unknown beyond alignments to predicted genes.
Given a set of biological sequences, the primary objective of sequence alignment is to predict the best overall mapping between the sequences, which accurately aligns the homologous regions that are embedded in them.
Therefore we first apply the sequence alignment algorithm to predict AMPs.
We used this structure together with multiple alignment information to predict a structure of the conserved region of CENP-V containing the Gfa domain (amino acids 126 260).
