Software and Tools Software and tools are provided as part of Leri Analytics suite software.

Find more details

We provide serveral pipelines base on our leri software suite at The released source code can be download HERE.

☘ Protein design in silico

Leri can be applied to facilitate the design of a given protein, and it attempts to uncover the biophysical rules (evolutionary information) that govern protein folding. You surely can launch leri to design your protein sequence if you already get the potentail computed from its MSA. Given a wild-type protein sequence, Leri infers mutants for both point mutation and multiple mutations with potential energy of each mutant that occur in the sequence. Here is an example of point mutaion on the human β2AR protein (PDB ID: 3PQR). leri evaluates the changes at signle site in stability of a protein without referring to its tertiary structure. Figure 1 illustrates energy changes (ΔE=Emut-Ewt) between wild type and mutant sequences when a single mutation occurs to the wild type sequence.

Figure 1. Point mutations based on statistical evolutionary potential.

Sibe computational tool

Sibe is a powerful digital engine, it can be used for biological science, particularly in protein dynamics & analyses and statistical analysis in protein sequences.

☘ Schematic chart

Figure 1. Protocols in Sibe.

☘ Statistics on protein sequences

Figure 2. (A) Sequence similarity, (B) Positional conservation, and (C) Mutant effect from the Kullback-Leibler relative entropy Di.

☘ Evolutionary Coupling Analysis (ECA)

Starting from the obtained MSA, we can analyze direct couplings between pairwise amino acids by independent component analysis for the human β2AR protein (PDB ID: 3PQR). According to top two eigenvalues that are statistically significant, the coupling are transformed into two independent components, which are defined as independent 'blocks' including groups of amino acids.

(A) Evolutionary coupling matrix plot

(B) Coupling blocks

Figure 3. Analysis on evolutionary couplings.

When Sibe completes the calculations on the MSA, you can collect the results in the default directory. Moreover, Sibe will also provide you serval bash script for plotting the figures. As illustrated in Figure 3(B), Sibe successfully obtained two 'blocks' for the example protein. Each block indicates a group of amino acids that are physically connected (part of the residues in green block as shown in Figure 4(A) are physically connected to each other) in the tertiary structure and may be functionally correlated.

(A) Coupled blocks

(B) The "blue" block

(C) The "red" block

Figure 3. Mapping evolutionary coupled blocks to the tertiary structure.

Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico. BMC bioinformatics, 20(1), 1-11.

Protein structure prediction

NiDelta sequence-specified smart system for predicting protein ptructure, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, NiDelta can predict the tertiary structures of a number of target proteins with remarkable accuracy.

☘ Schematic chart

Figure 1. De novo protein structure prediction.

Figure 2. De novo prediction for folding pathways of protein YiaD (PDB ID: 2K1S).

Figure 3. De novo prediction for folding pathways of protein tyrosine phosphatase (PDB ID: 1ZGG).

De novo protein structure prediction using ultra-fast molecular dynamics simulation. PloS one 13(11), p.e0205819, November 20, 2018.

Iteratively protein folding

iTooU is an approach for predicting protein folding pathways and tertiary structure from its primary sequence. By iteratively fixing secondary structure, hydrogen bonds and tertiary contacts, our model can predict the tertiary structure with high precision. The algorithm only samples Ramachandran maps by launching folding simulations, which are run with bias sampling strategies, such as employing ranges of backbone dihedral angles and incorporating contact maps generated from previous stages as constraints. The approach is capable of accurately predicting secondary and tertiary structures for several proteins, and the method also can be used for analysis of energy surfaces near transition states.

☘ Schematic chart

Figure 1. De novo protein folding and structure prediction.

☘ Folding pathways

Figure 2. Comparison of the RMSD distribution of approximately 500 final structures in each round between TerItFix (upper) and iTooU (lower) (columns from left to right are DesG, TOP7, and LOTOP, respectively).

Figure 3. Folding pathways for DesG, TOP7 and LOTOP, repectively.

Protein Structure Prediction: Integrating Monte Carlo and Molecular Dynamics Approaches with Biased Sampling based on Sequential Stabilization and Evolution. The 10th Midwest Conference on Protein Folding Assembly and Molecular Motions, May, 2015. [ PDF]


Cuckoo search algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this study presents a new variant of cuckoo search algorithm with non-homogeneous search strategies based on quantum mechanism to enhance search ability of the classical cuckoo search algorithm. Featured contributions in this study include: (1) quantum-based strategy is developed for non-homogeneous update laws; (2) we for the first time present a set of theoretical analyses on cuckoo search algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the cuckoo search algorithm and the proposed algorithm. On twenty-four benchmark functions, we compare our method with five existing cuckoo search based methods and other ten state-of-the-art algorithms. The numerical results demonstrate the proposed algorithm is significantly better than the original cuckoo search algorithm and the rest of compared methods according to two non-parametric tests.

Click here to download the source code.

Figure 1. Protocols in NoCuSa.

A Nonhomogeneous Cuckoo Search Algorithm based-on Quantum Mechanism for Real-Parameter Optimization. IEEE transactions on cybernetics 47(2): 391-402, 2016.

CHPSO & OptiFel

Designing an accurate and reliable T-S fuzzy model from the data-set has attracted a lot of attentions, and particle swarm optimization (PSO) algorithm has been widely used to formulate the model construction into a framework of optimization. However, classical PSO algorithm suffers the premature and is easily trapped in local optima, which will greatly decrease the model accuracy. To overcome these drawbacks, a multi-swarm PSO (CHPSO) with heterogeneous strategies is introduced in this study to improve the performance of PSO. In CHPSO, there are four cooperative sub-swarms that can share information from each other but maintain differences. Various sub-swarms execute different search mechanisms for the potential solutions, and we find that the cooperation among the various sub-swarms is helpful for keeping a good balance between the exploration and exploitation in the search process, making the particles capable of converging to stable points.

Click OptiFel and CHPSO to download the Matlab source codes.
Click CHPSO to download C++ package.

OptiFel: a Convergent Heterogeneous Particle Swarm Optimization for Takagi-Sugeno Fuzzy Modeling. IEEE Transactions on Fuzzy Systems, 22(4): 919-933, Aug. 2014.


A human readable fuzzy rule-based model, denoted as MoPath, is developed for the identification of both structure topology and associated parameters, simultaneously, of a biological network within an optimization framework. In MoPath, we model the nonlinear biological system with the Takagi-Sugeno (T-S) fuzzy rules, which is encoded as the particles of a designed convergent heterogeneous particle swarm optimization (CHPSO) algorithm to generate optimal solutions. The CHPSO maintains four cooperative sub-swarms to accomplish different search tasks for potential solutions. Based on the theoretical analysis, we demonstrate that the cooperation among the sub-warms can maintain a balance between exploration and exploitation to ensure the particles converge to stable points, which is greatly helpful for finding the optimal T-S fuzzy models consisting of both the network topology and parameters. We evaluate the proposed MoPath on two dynamic biological networks, and successfully generate a few human readable rules that can well represent the network with high accuracy and good robustness.

Click here to download the source code.

[1] Modeling Nonlinear Dynamic Biological Systems with Human-Readable Fuzzy Rules Optimized by Convergent Heterogeneous Particle Swarm . European Journal of Operational Research, 247(2): 349-358, 2015.
[2] OptiFel: a Convergent Heterogeneous Particle Swarm Optimization for Takagi-Sugeno Fuzzy Modeling. IEEE Transactions on Fuzzy Systems, 22(4): 919-933, Aug. 2014.

Get Our Professional Analytical Reports with Leri

Start Now