Aligning multiple biological sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences' structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. MSA also has many applications in Next-Generation Sequencing (NGS) data analysis such aligning multiple short reads. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this talk, I will describe a new scoring method for use in biological multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our three new multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge and sequence consistency to produce biological meaningful sequence alignments. The sequence knowledge-based algorithm utilizes the existing biological sequence knowledge databases such as Swiss-Prot to guide sequence alignment. When sequence knowledge databases are not available, the sequence consistency-based algorithm can utilize the consistency information from the input sequence to achieve a similar effect. Experimental results and theoretical analysis indicate that our new scoring function and alignment algorithms truly improve the current best multiple sequence alignment algorithms.