The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

/pdf/clustal-w-improving-the-sensitivity-of-progressive-multiple-2na2s9sq2h.pdf

Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Phaser is a program for phasing macromolecular crystal structures by both molecular replacement and experimental phasing methods. The novel phasing algorithms implemented in Phaser have been developed using maximum likelihood and multivariate statistics. For molecular replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solutions from noise, and for single-wavelength anomalous dispersion experimental phasing, the new algorithms, which account for correlations between F+ and F−, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ΔF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallographic community.

/pdf/phaser-crystallographic-software-2ceb03fha6.pdf

Phaser crystallographic software

Comparative protein modeling is increasingly gaining interest since it is of great assistance during the rational design of mutagenesis experiments. The availability of this method, and the resulting models, has however been restricted by the availability of expensive computer hardware and software. To overcome these limitations, we have developed an environment for comparative protein modeling that consists of SWISS-MODEL, a server for automated comparative protein modeling and of the SWISS-PdbViewer, a sequence to structure workbench. The Swiss-PdbViewer not only acts as a client for SWISS-MODEL, but also provides a large selection of structure analysis and display tools. In addition, we provide the SWISS-MODEL Repository, a database containing more than 3500 automatically generated protein models. By making such tools freely available to the scientific community, we hope to increase the use of protein structures and models in the process of experiment design.

SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

/pdf/highly-accurate-protein-structure-prediction-with-alphafold-2xjbzfwu3n.pdf

Highly accurate protein structure prediction with AlphaFold

Homologous proteins have regions which retain the same general fold and regions where the folds differ. For pairs of distantly related proteins (residue identity approximately 20%), the regions with the same fold may comprise less than half of each molecule. The regions with the same general fold differ in structure by amounts that increase as the amino acid sequences diverge. The root mean square deviation in the positions of the main chain atoms, delta, is related to the fraction of mutated residues, H, by the expression: delta(A) = 0.40 e1.87H.

/pdf/the-relation-between-the-divergence-of-sequence-and-xk5asmod0u.pdf

The relation between the divergence of sequence and structure in proteins.

Canonical structures for the hypervariable regions of immunoglobulins

On the basis of comparative studies of known antibody structures and sequences it has been argued that there is a small repertoire of main-chain conformations for at least five of the six hypervariable regions of antibodies, and that the particular conformation adopted is determined by a few key conserved residues. These hypotheses are now supported by reasonably successful predictions of the structures of most hypervariable regions of various antibodies, as revealed by comparison with their subsequently determined structures.

Conformations of immunoglobulin hypervariable regions.

'Algorithmic Aspects' 06-13 The posterior decoding computes the most probable state at any position given an observation (it is taken over all possible sequences that fit the observation). The Viterby algorithm computes the most probable state sequence for the observation. (Note that the sequence of most probable states is usually not equal to the most probable state sequence.) 05-13 The Viterbi algorithm is a form of dynamic programming. For each state p ∈ Q of the model, and each position i ≤ n of the observation x 1. .. x n , compute the maximal probability v q (i) of a state sequence ending in p with the observation upto position i. In fact, v q (i) = max p∈Q v p (i − 1)t pq e qx i , where t pq are the transition probabilities, and e qσ the emission probabilities. 05-14 Uniform clock implies all leaves are at the same distance from the root. The maximal distance is between two nodes that are only connected via the root. Here that distance is 18, so all leaves are 9 below the root. As 1 & 4 have maximal distance to 2,3 & 5, this means that the tree can be divided at the root into two subtrees, with 1,4 in one and 2,3,5 in the other subtree. Now continue recursively. The distance of 1&4 is 8, which implies they are distance 4 below their common ancestor. This ancestor then is 5 below the root. Now consider the matrix restricted to 2,3,5. Max distance now is 12, between 2 and 3 & 5. Thus 2 is 6 below its ancestor, which is 3 below the root; 3 & 5 are 4 apart, so 2 below their ancestor, which leaves 4 for the distance to the node where the branch to 2 was. 1 2 3 4 5 4 4 5 3 6 4 2 2 05-15 First we build the trie, a tree labelled with letters along the edges, spelling the words from our 'dictionary' AACG, ACA, CACG, CG. Usually we mark the nodes where words from the dictionart end, since they can end in internal nodes if one word is the prefix of another. Here this is not necessary as all words end in a leaf. A C A C G C A A C G G Now determine failure links. Each node links to the longest prefix …

Computational molecular biology

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.

/pdf/prediction-of-protein-function-from-protein-sequence-and-1x3ddufwc1.pdf

Arthur M. Lesk

Papers

The relation between the divergence of sequence and structure in proteins.

Canonical structures for the hypervariable regions of immunoglobulins

Conformations of immunoglobulin hypervariable regions.

Computational molecular biology

Prediction of protein function from protein sequence and structure.